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• How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?
• What are young women’s experiences on dating sites?
• What are non-experts’ ideas and opinions about climate change?
• How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analyzing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Discourse analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

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• Peer review
• on behalf of the Coproduction Laboratory
• 1 Dartmouth Health, Lebanon, NH, USA
• 2 Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
• 3 Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
• 4 Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
• 5 Highland Park, NJ, USA
• 6 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
• Correspondence to: C H Saunders catherine.hylas.saunders{at}dartmouth.edu
• Accepted 26 April 2023

Qualitative research methods explore and provide deep contextual understanding of real world issues, including people’s beliefs, perspectives, and experiences. Whether through analysis of interviews, focus groups, structured observation, or multimedia data, qualitative methods offer unique insights in applied health services research that other approaches cannot deliver. However, many clinicians and researchers hesitate to use these methods, or might not use them effectively, which can leave relevant areas of inquiry inadequately explored. Thematic analysis is one of the most common and flexible methods to examine qualitative data collected in health services research. This article offers practical thematic analysis as a step-by-step approach to qualitative analysis for health services researchers, with a focus on accessibility for patients, care partners, clinicians, and others new to thematic analysis. Along with detailed instructions covering three steps of reading, coding, and theming, the article includes additional novel and practical guidance on how to draft effective codes, conduct a thematic analysis session, and develop meaningful themes. This approach aims to improve consistency and rigor in thematic analysis, while also making this method more accessible for multidisciplinary research teams.

Through qualitative methods, researchers can provide deep contextual understanding of real world issues, and generate new knowledge to inform hypotheses, theories, research, and clinical care. Approaches to data collection are varied, including interviews, focus groups, structured observation, and analysis of multimedia data, with qualitative research questions aimed at understanding the how and why of human experience. 1 2 Qualitative methods produce unique insights in applied health services research that other approaches cannot deliver. In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many researchers are unsure how to apply them or consider them too time consuming to be useful in responding to practical challenges 7 or pressing situations such as public health emergencies. 8 Consequently, researchers might hesitate to use them, or use them improperly. 9 10 11

Although much has been written about how to perform thematic analysis, practical guidance for non-specialists is sparse. 3 5 6 12 13 In the multidisciplinary field of health services research, qualitative data analysis can confound experienced researchers and novices alike, which can stoke concerns about rigor, particularly for those more familiar with quantitative approaches. 14 Since qualitative methods are an area of specialisation, support from experts is beneficial. However, because non-specialist perspectives can enhance data interpretation and enrich findings, there is a case for making thematic analysis easier, more rapid, and more efficient, 8 particularly for patients, care partners, clinicians, and other stakeholders. A practical guide to thematic analysis might encourage those on the ground to use these methods in their work, unearthing insights that would otherwise remain undiscovered.

Given the need for more accessible qualitative analysis approaches, we present a simple, rigorous, and efficient three step guide for practical thematic analysis. We include new guidance on the mechanics of thematic analysis, including developing codes, constructing meaningful themes, and hosting a thematic analysis session. We also discuss common pitfalls in thematic analysis and how to avoid them.

Summary points

Qualitative methods are increasingly valued in applied health services research, but multidisciplinary research teams often lack accessible step-by-step guidance and might struggle to use these approaches

A newly developed approach, practical thematic analysis, uses three simple steps: reading, coding, and theming

Based on Braun and Clarke’s reflexive thematic analysis, our streamlined yet rigorous approach is designed for multidisciplinary health services research teams, including patients, care partners, and clinicians

This article also provides companion materials including a slide presentation for teaching practical thematic analysis to research teams, a sample thematic analysis session agenda, a theme coproduction template for use during the session, and guidance on using standardised reporting criteria for qualitative research

In their seminal work, Braun and Clarke developed a six phase approach to reflexive thematic analysis. 4 12 We built on their method to develop practical thematic analysis ( box 1 , fig 1 ), which is a simplified and instructive approach that retains the substantive elements of their six phases. Braun and Clarke’s phase 1 (familiarising yourself with the dataset) is represented in our first step of reading. Phase 2 (coding) remains as our second step of coding. Phases 3 (generating initial themes), 4 (developing and reviewing themes), and 5 (refining, defining, and naming themes) are represented in our third step of theming. Phase 6 (writing up) also occurs during this third step of theming, but after a thematic analysis session. 4 12

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

For use by practicing clinicians, patients and care partners, students, interdisciplinary teams, and those new to qualitative research

When important insights from healthcare professionals are inaccessible because they do not have qualitative methods training

When time and resources are limited

Steps in practical thematic analysis

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We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

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Depending on the nature of the data, codes might either fall into flat categories or be arranged hierarchically. Flat categories are most common when the data deal with topics on the same conceptual level. In other words, one topic is not a subset of another topic. By contrast, hierarchical codes are more appropriate for concepts that naturally fall above or below each other. Hierarchical coding can also be a useful form of data management and might be necessary when working with a large or complex dataset. 5 Codes grouped into these categories can also make it easier to naturally transition into generating themes from the initial codes. 5 These decisions between flat versus hierarchical coding are part of the work of the coding team. In both cases, coders should ensure that their code structures are guided by their research questions.

Developing the codebook

A codebook is a shared document that lists code labels and comprehensive descriptions for each code, as well as examples observed within the data. Good code descriptions are precise and specific so that coders can consistently assign the same codes to relevant data or articulate why another coder would do so. Codebook development is iterative and involves input from the entire coding team. However, as those closest to the data, coders must resist undue influence, real or perceived, from other team members with conflicting opinions—it is important to mitigate the risk that more senior researchers, like principal investigators, exert undue influence on the coders’ perspectives.

In practical thematic analysis, coders begin codebook development by independently coding a small portion of the data, such as two to three transcripts or other units of analysis. Coders then individually produce their initial codebooks. This task will require them to reflect on, organise, and clarify codes. The coders then meet to reconcile the draft codebooks, which can often be difficult, as some coders tend to lump several concepts together while others will split them into more specific codes. Discussing disagreements and negotiating consensus are necessary parts of early data analysis. Once the codebook is relatively stable, we recommend soliciting input on the codes from all manuscript authors. Yet, coders must ultimately be empowered to finalise the details so that they are comfortable working with the codebook across a large quantity of data.

Assigning codes to the data

After developing the codebook, coders will use it to assign codes to the remaining data. While the codebook’s overall structure should remain constant, coders might continue to add codes corresponding to any new concepts observed in the data. If new codes are added, coders should review the data they have already coded and determine whether the new codes apply. Qualitative data analysis software can be useful for editing or merging codes.

We recommend that coders periodically compare their code occurrences ( box 5 ), with more frequent check-ins if substantial disagreements occur. In the event of large discrepancies in the codes assigned, coders should revise the codebook to ensure that code descriptions are sufficiently clear and comprehensive to support coding alignment going forward. Because coding is an iterative process, the team can adjust the codebook as needed. 5 28 29

Quantitative coding in context

Researchers should generally avoid reporting code counts in thematic analysis. However, counts can be a useful proxy in maintaining alignment between coders on key concepts. 26 In practice, therefore, researchers should make sure that all coders working on the same piece of data assign the same codes with a similar pattern and that their memoing and overall assessment of the data are aligned. 37 However, the frequency of a code alone is not an indicator of its importance. It is more important that coders agree on the most salient points in the data; reviewing and discussing summary memos can be helpful here. 5

Researchers might disagree on whether or not to calculate and report inter-rater reliability. We note that quantitative tests for agreement, such as kappa statistics or intraclass correlation coefficients, can be distracting and might not provide meaningful results in qualitative analyses. Similarly, Braun and Clarke argue that expecting perfect alignment on coding is inconsistent with the goal of co-constructing meaning. 28 29 Overall consensus on codes’ salience and contributions to themes is the most important factor.

Definition of themes

Themes are meta-constructs that rise above codes and unite the dataset ( box 6 , fig 2 ). They should be clearly evident, repeated throughout the dataset, and relevant to the research questions. 38 While codes are often explicit descriptions of the content in the dataset, themes are usually more conceptual and knit the codes together. 39 Some researchers hypothesise that theme development is loosely described in the literature because qualitative researchers simply intuit themes during the analytical process. 39 In practical thematic analysis, we offer a concrete process that should make developing meaningful themes straightforward.

Themes in context

According to Braun and Clarke, a theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

• Ziebland S ,
• ↵ A Hybrid Approach to Thematic Analysis in Qualitative Research: Using a Practical Example. 2018. https://methods.sagepub.com/case/hybrid-approach-thematic-analysis-qualitative-research-a-practical-example .
• Maguire M ,
• Vindrola-Padros C ,
• Vindrola-Padros B
• ↵ Vindrola-Padros C. Rapid Ethnographies: A Practical Guide . Cambridge University Press 2021. https://play.google.com/store/books/details?id=n80HEAAAQBAJ
• Schroter S ,
• Merino JG ,
• Barbeau A ,
• ↵ Padgett DK. Qualitative and Mixed Methods in Public Health . SAGE Publications 2011. https://play.google.com/store/books/details?id=LcYgAQAAQBAJ
• Scharp KM ,
• Korstjens I
• Barnett-Page E ,
• ↵ Guest G, Namey EE, Mitchell ML. Collecting Qualitative Data: A Field Manual for Applied Research . SAGE 2013. https://play.google.com/store/books/details?id=-3rmWYKtloC
• Sainsbury P ,
• Emerson RM ,
• Saunders B ,
• Kingstone T ,
• Hennink MM ,
• Kaiser BN ,
• Hennink M ,
• O’Connor C ,
• ↵ Yen RW, Schubbe D, Walling L, et al. Patient engagement in the What Matters Most trial: experiences and future implications for research. Poster presented at International Shared Decision Making conference, Quebec City, Canada. July 2019.
• ↵ Got questions about Thematic Analysis? We have prepared some answers to common ones. https://www.thematicanalysis.net/faqs/ (accessed 9 Nov 2022).
• ↵ Braun V, Clarke V. Thematic Analysis. SAGE Publications. 2022. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481 .
• Kalpokas N ,
• Radivojevic I
• Campbell KA ,
• Durepos P ,
• ↵ Understanding Thematic Analysis. https://www.thematicanalysis.net/understanding-ta/ .
• Saunders CH ,
• Stevens G ,
• CONFIDENT Study Long-Term Care Partners
• MacQueen K ,
• Vaismoradi M ,
• Turunen H ,
• Schott SL ,
• Berkowitz J ,
• Carpenter-Song EA ,
• Goldwag JL ,
• Durand MA ,
• Goldwag J ,
• Saunders C ,
• Mishra MK ,
• Rodriguez HP ,
• Shortell SM ,
• Verdinelli S ,
• Scagnoli NI
• Campbell C ,
• Sparkes AC ,
• McGannon KR
• Sandelowski M ,
• Connelly LM ,
• O’Malley AJ ,

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• v.21(12); 2021 Dec

General-purpose thematic analysis: a useful qualitative method for anaesthesia research

1 Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland, Auckland, New Zealand

2 Department of Anaesthesia, Auckland City Hospital, Auckland, New Zealand

Learning objectives

By reading this article, you should be able to:

• • Explain when to use thematic analysis.
• • Describe the steps in thematic analysis of interview data.
• • Critique the quality of a study that uses the method of thematic analysis.
• • Thematic analysis is a popular method for systematically analysing qualitative data, such as interview and focus group transcripts.
• • It is one of a cluster of methods that focus on identifying patterns of meaning, or themes, across a data set.
• • It is relevant to many questions in perioperative medicine and a good starting point for those new to qualitative research.
• • Systematic approaches to thematically analysing data exist, with key components to demonstrate rigour, accountability, confirmability and reliability.
• • In one study, a useful six-step approach to analysing data is offered.

Anaesthesia research commonly uses quantitative methods, such as surveys, RCTs or observational studies. Such methods are often concerned with answering what questions and how many questions. Qualitative research is more concerned with why questions that enable us to understand social complexities. ‘Qualitative studies in the anaesthetic setting’, write Shelton and colleagues, ‘have been used to define excellence in anaesthesia, explore the reasons behind drug errors, investigate the acquisition of expertise and examine incentives for hand hygiene in the operating theatre’. 1

General-purpose thematic analysis (termed thematic analysis hereafter) is a qualitative research method commonly used with interview and focus group data to understand people's experiences, ideas and perceptions about a given topic. Thematic analysis is a good starting point for those new to qualitative research and is relevant to many questions in the perioperative context. It can be used to understand the experiences of healthcare professionals and patients and their families. Box 1 gives examples of questions amenable to thematic analysis in anaesthesia research.

Examples of questions amenable to thematic analysis.

• (i) How do operating theatre staff feel about speaking up with their concerns?
• (ii) What are trainee's conceptions of the balance between service and learning?
• (iii) What are patients' experiences of preoperative neurocognitive screening?

Alt-text: Box 1

Thematic analysis involves a process of assigning data to a number of codes, grouping codes into themes and then identifying patterns and interconnections between these themes. 2 Thematic analysis allows for a nuanced understanding of what people say and do within their particular social contexts. Of note, thematic analysis can be used with interviews and focus groups and other sources of data, such as documents or images.

Thematic analysis is not the same as content analysis. Content analysis involves counting the frequency with which words or phrases appear in data. Content analysis is a method used to code and categorise textual information systematically to determine trends, frequency and patterns of words used. 3 Conversely, thematic analysis focuses on the relative importance of ideas and how ideas connect and govern practices. Thematic analysis does not rely on frequency counts to indicate the importance of coded data. Content analysis can be coupled with thematic analysis, where both themes and frequencies of particular statements or words are reported.

Thematic analysis is a research method, not a methodology. A methodology is a method with a philosophical underpinning. If researchers report only on what they did, this is the method. If, in addition, they report on the philosophy that governed what they did, this is methodology. Common methodologies in qualitative research include phenomenology, grounded theory, hermeneutics, narrative enquiry and ethnography. 4 Each of these methodologies has associated methods for data analysis. Thematic analysis can be combined with many different qualitative methodologies.

There are also different types of thematic analysis, such as inductive (including general purpose), applied, deductive or semantic thematic analysis. Inductive analysis involves approaching the data with an open mind, inductively looking for patterns and themes and interpreting these for meaning. 2 , 4 Of note, researchers can never have a truly open mind on their topic of interest, so the process will be influenced by their particular perspectives, which need to be declared. In applied and deductive thematic analysis, the researcher will have a pre-existing framework (which may be informed by theory or philosophy) against which they will attempt to categorise the data. 4 , 5 , 6 For semantic thematic analysis, the data are coded on explicit content, and tend to be descriptive rather than interpretative. 6

In this review, we outline what thematic analysis entails and when to use it. We also list some markers to look for to appraise the quality of a published study.

Designing the data collection

Before embarking on qualitative research, as with quantitative research, it is important to seek ethical review of the proposed study. Ethical considerations include such issues as consent, data security and confidentiality, permission to use quotes, potential for identifying individuals or institutions, risk of psychological harm to participants with studies on sensitive issues (e.g. suicide or sexual harassment), power relationships between interviewer and interviewee or intrusion on other activities (such as teaching time or work commitments). 7

Qualitative research often involves asking people questions during interviews or focus groups. Merriam and Tisdell stated that, ‘The most common form of interview is the person-to-person encounter in which one person elicits information from the other’. 8 Information is elicited through careful and purposeful questioning and listening. 9 Research interviews in anaesthesia are generally purposeful conversations with a structure that allows the researcher to gather information about a participant's ideas, perceptions and experiences concerning a given topic.

A structured interview is when the researcher has already decided on a set of questions to ask. 9 If the researcher will ask a set of questions, but has flexibility to follow up responses with further questions, this is called a semi-structured interview. Semi-structured interviews are commonly used in research involving thematic analysis. The researcher can also use other forms of questioning, such as single-question interview. Semi-structured interviews are commonly used in anaesthesia, such as the studies from our own research group. 10 , 11 , 12

Interviews are usually recorded in audio form and then transcribed. For each interview or focus group, a single transcript is created. The transcripts become the written form of data and the collection of transcripts from the research participants becomes the data set.

Designing productive interview questions

The design of interview questions significantly shapes a participant's response. Interview questions should be designed using ‘sensitising concepts’ to encourage participants to share information that will increase a researcher's understanding of the participants' experiences, views, beliefs and behaviours. 13 ‘Sensitising concepts’ describe words in questions that bring the participants' attention to a concept of research interest. Examples of sensitising concepts include speaking up, teamwork and theoretical concepts (such as Kolb's experiential learning cycle or Foucauldian power theory in relation to trainee learning and operating theatre culture). 14 , 15 Specifically, the questions should be framed in such a way as to encourage participants to make sense of their own experience and in their own words. The researcher should try to minimise the influences of their own biases when they design questions. Using open-ended questions will increase the richness of data. Box 2 gives examples of question design.

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

Bias is an inclination or prejudice for or against someone or something, whereas positionality is a person's position in society or their stance towards someone or something. For example, Tanisha once had an inexperienced anaesthetist accidentally rupture one of her veins whilst they were siting an i.v. cannula in an emergency situation. Now, Tanisha has a bias against inexperienced anaesthetists. Tanisha's positionality —a medical anthropologist with no anaesthesia training, but working with many anaesthesia colleagues, including her director—may also inform that bias or the way that Tanisha interacts with anaesthetists. Reflexivity is a process whereby people/researchers proactively reflect on their biases and positionality. Biases shape positionality (i.e. the stance of the researcher in relation to the social, historical and political contexts of the study). In practical research terms, biases and positionality inform the way researchers design and undertake research, and the way they interpret data. It is important in qualitative research to both identify biases and positionality, and to take steps to minimise the impact of these on the research.

Some ways to minimise the influence of bias and positionality on findings include:

(i) Raise awareness amongst the research team of bias and positionality.

(ii) Design research/interview questions that minimise potential for these to distort which data are collected or how they are collected.

(iii) Researchers ask reflexive questions during data analysis, such as, ‘Is my bias about xxx informing my view of these data?’

(iv) Two or more researchers are involved in the analysis process.

(v) Data analysis member check (e.g. checking back with participants if the interpretation of their data is consistent with their experience and with what they said).

Before embarking on the study, researchers should consider their own experiences, knowledge and views; how this influences their own position in relation to the study question; and how this position could potentially introduce bias in how they collect and analyse the data. Taking time to reflect on the impact of the researchers' position is an important step towards being reflective and transparent throughout the research process. When writing up the study, researchers should include statements on bias and positionality. In quantitative research, we aim to eliminate bias. In qualitative research, we acknowledge that bias is inevitable (and sometimes even unconscious), and we take steps to make it explicit and to minimise its effect on study design and data interpretation.

Sampling and saturation

Qualitative research typically uses systematic, non-probability sampling. Unlike quantitative research, the goal of sampling is not to randomly select a representative sample from a population. Instead, researchers identify and select individuals or groups relevant to the research question. Commonly used sampling techniques in anaesthesia qualitative research are homogeneous (group) sampling and maximum variation sampling. In the former, researchers may be concerned with the experiences of participants from a distinct group or who share a certain characteristic (e.g. female anaesthesia trainees), so they recruit selectively from within the group with this shared characteristic to gain a rich, in-depth understanding of their experiences. Conversely, the aim with maximum variation sampling is to recruit participants with diverse characteristics to obtain a broad understanding of the question being studied (e.g. members of different professional groups within operating theatre teams, who have diverse ages, gender and ethnicities).

As with quantitative research, the purpose of sampling is to recruit sufficient numbers of participants to enable identification of patterns or richness in what they say or do to understand or explain the phenomenon of interest, and where collecting more data is unlikely to change this understanding.

In qualitative research, data collection and analysis often occur concurrently. This is because data collection is an iterative process both in recruitment and in questioning. The researchers may identify that more data are needed from a particular demographic group or on a particular theme to reach data saturation, so the next participants may be selected from a particular demographic, or be asked slightly different questions or probes to draw out that theme. Sample size is considered adequate when little or no new information emerges from interviews or focus groups; this is generally termed ‘data saturation’, although some qualitative researchers use the term ‘data sufficiency’. This could also be explained in terms of data reliability (i.e. the researcher is satisfied that collecting more data will not substantially change the results). Data saturation typically occurs with between 12 and 17 participants in a relatively homogeneous sampling, but larger numbers may be required, where the interviewees are from distinct groups or cultures. 16 , 17

Data management

For data sets that involve 10 or more transcripts or lengthy interviews (e.g. 90 min or more), researchers often use software to help them collate and manage the data. The most commonly used qualitative software packages are QSR NVivo, Atlas and Dedoose. 18 , 19 , 20 Many researchers use Microsoft Excel instead, or for small data sets the analysis can be done by hand, with pen, paper and scissors (i.e. researchers cut up printed transcripts and reorder the information according to code and theme). 21 NVivo and Atlas are simply repositories, in which you can input the transcripts and, using your coding scheme, sort the text into codes. They facilitate the task of analysis, rather than doing the analysis for you. Some advantages over coding by hand are that text can be allocated to more than one code, and you can easily identify the source of the segment of text you have coded.

Data analysis

Qualitative data analysis is ‘the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it’. 22

Several social scientists have described this analytical process in depth. 2 , 6 , 22 , 23 , 24 , 25 For inductive studies, we recommend researchers follow Braun and Clarke's practical six-phase approach to thematic analysis. 26 The phases are (i) familiarising the researcher with the data, (ii) generating initial codes, (iii) searching for themes, (iv) reviewing themes, (v) defining and naming themes and (vi) producing the report. These six phases are described next.

Phase 1: familiarising the researcher with the data

In this step, the researchers read the transcripts to become familiar with them and take notes on potential recurring ideas or potential themes. They share and discuss their ideas and, in conjunction with any sensitising concepts, they start thinking about possible codes or themes.

Phase 2: generating initial codes

The first step in Phase 2 is ‘assigning some sort of short-hand designation to various aspects of your data so that you can easily retrieve specific pieces of the data’. 2 The designation might be a word or a short phrase that summarises or captures the essence of a particular piece of text. Coding makes it easier to summarise and compare, which is important because qualitative research is primarily about synthesis and comparison of data. 2 , 25 As the researcher reads through the data, they assign codes. If they are coding a transcript, they might highlight some words, for example, and attach to them a single word that summarises their meaning.

Researchers undertaking thematic analysis should iteratively develop a ‘coding scheme’, which is essentially a list of the codes they create as they read the data, and definitions for each code. 25 , 26 Code definitions are important, as they help the researcher make decisions on whether to assign this code or another one to a segment of data. In Table 1 , we have provided an example of text data in Column 1. TJ analysed these data. To do so, she asked, ‘What are these data about? How does it answer the research question? What is the essence of this statement?’ She underlined keywords and created codes and definitions (Columns 2 and 3). Then, TJ searched the remaining data to see if any more data met each code definition, and if so, coded that (see Table 1 ). As demonstrated in Table 1 , data can be coded to multiple codes.

Table 1

How to code qualitative data: an example

In thematic analysis of interview data, we recommend that code definitions begin with something objective, such as ‘participant describes’. This keeps the researcher's focus on what participants said rather than what the researcher thought or said.

There is no set rule for how many codes to create. 25 However, in our experience, effective manageable coding schemes tend to have between 15 and 50 codes. The coding scheme is iterative. This means that the coding scheme is developed over time, with new codes being created as more data are coded. For example, after a close reading of the first transcript, the researcher might create, say, 10 codes that convey the key points. Then, the researcher reads and codes the next transcript and may, for instance, create additional four codes. As additional transcripts are read and coded, more codes may be created. Not all codes are relevant to all transcripts. The researcher will notice patterns as they code more transcripts. Some codes may be too broad and will need to be refined into two or three smaller codes (and vice versa ). Once the coding scheme is deemed complete and all transcripts have been coded, the researcher should go back to the beginning and recode the first few transcripts to ensure coding rigour.

The second step in Phase 2, once the coding is complete, is to collate all the data relevant to each of these codes.

Phase 3: searching for themes

In this phase, the researchers look across the codes to identify connections between them, with the intention of collating the codes into possible themes. Once these possible themes have been identified, all the data relevant to each possible theme are pulled together under that theme.

Phase 4: reviewing the themes

After the initial collation of the data into themes, the researchers undertake a rigorous process of checking the integrity of these themes, through reading and re-reading their data. This process includes checking to see if the themes ‘fit’ in relation to the coded excerpts (i.e. Do all the data collected under that theme fit within that theme?). Next is checking if the themes fit in relation to the whole data set (i.e. Do the themes adequately reflect the data?) This step may result in the search for additional themes. As a final step in this phase, the researchers create a thematic ‘map’ of the analysis.

When viewed together, the themes should answer the research question and should summarise participant experiences, views or behaviours.

Phase 5: naming the themes

Once researchers have checked the themes and included any additional emerging themes they name the final set of themes identified. Each theme and any subthemes should be listed in turn.

Phase 6: producing the report

The report should summarise the themes and illustrate them by choosing vivid or persuasive extracts from the data. For data arising from interviews, extracts will be quotes from participants. In some studies, researchers also report strong associations between themes, or divide a theme into sub-themes.

Tight word limits on many academic journals can make it difficult to include multiple quotes in the text. 27 One way around a word limit is to provide quotes in a table or a supplementary file, although quotes within the text tend to make for more interesting and compelling reading.

Who should analyse the data?

Ideally, each researcher in the team should be involved in the data analysis. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias. Independent analysis is time and resource intensive. In clinical research, close independent analysis by each member of the research team may be impractical, and one or two members may undertake the analysis while the rest of the research team read sections of data (e.g. reading two or three transcripts rather than closely analysing the whole data set), thus contributing to Phase 1 and Phase 2 of Braun and Clarke's method. 2

The research team should regularly meet to discuss the analytical process, as described earlier, to workshop and reach agreement on the coding and emergent themes (Phase 4 and Phase 5). The research team members compare their perspectives on the data, analyse divergences and coincidences and reach agreement on codes and emerging themes. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias.

Judging the quality and rigour of published studies involving thematic analysis

There are a number of indicators of quality when reading and appraising studies. 28 , 29 , 30 , 31 In essence, the authors should clearly state their method of analysis (e.g. thematic analysis) and should reference the literature relevant to their qualitative method, for example Braun and Clarke. 2 This is to indicate that they are following established steps in thematic analysis. The authors should include in the methods a description of the research team, their biases and experience and the efforts made to ensure analytical rigour. Verbatim quotes should be included in the findings to provide evidence to support the themes.

A number of guides have been published to assist readers, researchers and reviewers to evaluate the quality of a qualitative study. 30 , 31 The Joanna Briggs Institute guide to critical appraisal of qualitative studies is a good start. 30 This guide includes a set of 10 criteria, which can be used to rate the study. The criteria are summarised in Box 3 . Within these criteria lie rigorous methodological approaches to how data are collected, analysed and interpreted.

Ten quality appraisal criteria for qualitative literature.31

• (i) Alignment between the stated philosophical perspective and the research methodology
• (ii) Alignment between the research methodology and the research question or objectives
• (iii) Alignment between the research methodology and the methods used to collect data
• (iv) Alignment between the research methodology and the representation and analysis of data
• (v) Alignment between the research methodology and the interpretation of results
• (vi) A statement locating the researcher culturally or theoretically (positionality and bias)
• (vii) The influence of the researcher on the research, and vice versa
• (viii) Adequate representation of participants and their voices
• (ix) Ethical research conduct and evidence of ethical approval by an appropriate body
• (x) Conclusions flow from the analysis, or interpretation, of the data

Alt-text: Box 3

Another approach to quality appraisal comes from Lincoln and Guba, who have published widely on the topic of judging qualitative quality. 28 They look for quality in terms of credibility, transferability, dependability, confirmability and authenticity. There are many qualitative checklists readily accessible online, such as the Standards for Reporting Qualitative Research checklist or the Consolidated Criteria for Reporting Qualitative Research checklist, which researchers can include in their work to demonstrate quality in these areas.

Conclusions

As with quantitative research, qualitative research has requirements for rigour and trustworthiness. Thematic analysis is an accessible qualitative method that can offer researchers insight into the shared experiences, views and behaviours of research participants.

Declaration of interests

The authors declare that they have no conflicts of interest.

The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education .

Biographies

Tanisha Jowsey PhD BA (Hons) MA PhD is a senior lecturer in the Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland. She has a background in medical anthropology and has expertise as a qualitative researcher.

Carolyn Deng MPH FANZCA is a specialist anaesthetist at Auckland City Hospital. She has a Master of Public Health degree. She is embarking on qualitative research in perioperative medicine and hopes to use it as a tool to complement quantitative research findings in the future.

Jennifer Weller MD MClinEd FANZCA FRCA is head of the Centre for Medical and Health Sciences Education at the University of Auckland. Professor Weller is a specialist anaesthetist at Auckland City Hospital and often uses qualitative methods in her research in clinical education, teamwork and patients' safety.

Matrix codes: 1A01, 2A01, 3A01

Thematic Analysis: A Step by Step Guide

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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What is Thematic Analysis?

Thematic analysis is a qualitative research method used to identify, analyze, and interpret patterns of shared meaning (themes) within a given data set, which can be in the form of interviews , focus group discussions , surveys, or other textual data.

Thematic analysis is a useful method for research seeking to understand people’s views, opinions, knowledge, experiences, or values from qualitative data.

This method is widely used in various fields, including psychology, sociology, and health sciences.

Thematic analysis minimally organizes and describes a data set in rich detail. Often, though, it goes further than this and interprets aspects of the research topic.

Key aspects of Thematic Analysis include:

• Flexibility : It can be adapted to suit the needs of various studies, providing a rich and detailed account of the data.
• Coding : The process involves assigning labels or codes to specific segments of the data that capture a single idea or concept relevant to the research question.
• Themes : Representing a broader level of analysis, encompassing multiple codes that share a common underlying meaning or pattern. They provide a more abstract and interpretive understanding of the data.
• Iterative process : Thematic analysis is a recursive process that involves constantly moving back and forth between the coded extracts, the entire data set, and the thematic analysis being produced.
• Interpretation : The researcher interprets the identified themes to make sense of the data and draw meaningful conclusions.

It’s important to note that the types of thematic analysis are not mutually exclusive, and researchers may adopt elements from different approaches depending on their research questions, goals, and epistemological stance.

The choice of approach should be guided by the research aims, the nature of the data, and the philosophical assumptions underpinning the study.

1. Coding Reliability Thematic Analysis

Coding reliability TA emphasizes using coding techniques to achieve reliable and accurate data coding, which reflects (post)positivist research values.

This approach emphasizes the reliability and replicability of the coding process. It involves multiple coders independently coding the data using a predetermined codebook.

The goal is to achieve a high level of agreement among the coders, which is often measured using inter-rater reliability metrics.

This approach often involves a coding frame or codebook determined in advance or generated after familiarization with the data.

In this type of TA, two or more researchers apply a fixed coding frame to the data, ideally working separately.

Some researchers even suggest that at least some coders should be unaware of the research question or area of study to prevent bias in the coding process.

Statistical tests are used to assess the level of agreement between coders, or the reliability of coding. Any differences in coding between researchers are resolved through consensus.

This approach is more suitable for research questions that require a more structured and reliable coding process, such as in content analysis or when comparing themes across different data sets.

2. Codebook Thematic Analysis

Codebook TA, such as template, framework, and matrix analysis, combines elements of coding reliability and reflexive.

Codebook TA, while employing structured coding methods like those used in coding reliability TA, generally prioritizes qualitative research values, such as reflexivity.

In this approach, the researcher develops a codebook based on their initial engagement with the data. The codebook contains a list of codes, their definitions, and examples from the data.

The codebook is then used to systematically code the entire data set. This approach allows for a more detailed and nuanced analysis of the data, as the codebook can be refined and expanded throughout the coding process.

It is particularly useful when the research aims to provide a comprehensive description of the data set.

Codebook TA is often chosen for pragmatic reasons in applied research, particularly when there are predetermined information needs, strict deadlines, and large teams with varying levels of qualitative research experience

The use of a codebook in this context helps to map the developing analysis, which is thought to improve teamwork, efficiency, and the speed of output delivery.

3. Reflexive Thematic Analysis

This approach emphasizes the role of the researcher in the analysis process. It acknowledges that the researcher’s subjectivity, theoretical assumptions, and interpretative framework shape the identification and interpretation of themes.

In reflexive TA, analysis starts with coding after data familiarization. Unlike other TA approaches, there is no codebook or coding frame. Instead, researchers develop codes as they work through the data.

As their understanding grows, codes can change to reflect new insights—for example, they might be renamed, combined with other codes, split into multiple codes, or have their boundaries redrawn.

If multiple researchers are involved, differences in coding are explored to enhance understanding, not to reach a consensus. The finalized coding is always open to new insights and coding.

Reflexive thematic analysis involves a more organic and iterative process of coding and theme development. The researcher continuously reflects on their role in the research process and how their own experiences and perspectives might influence the analysis.

This approach is particularly useful for exploratory research questions and when the researcher aims to provide a rich and nuanced interpretation of the data.

Six Steps Of Thematic Analysis

The process is characterized by a recursive movement between the different phases, rather than a strict linear progression.

This means that researchers might revisit earlier phases as their understanding of the data evolves, constantly refining their analysis.

For instance, during the reviewing and developing themes phase, researchers may realize that their initial codes don’t effectively capture the nuances of the data and might need to return to the coding phase. 

This back-and-forth movement continues throughout the analysis, ensuring a thorough and evolving understanding of the data

Step 1: Familiarization With the Data

Familialization is crucial, as it helps researchers figure out the type (and number) of themes that might emerge from the data.

Familiarization involves immersing yourself in the data by reading and rereading textual data items, such as interview transcripts or survey responses.

You should read through the entire data set at least once, and possibly multiple times, until you feel intimately familiar with its content.

• Read and re-read the data (e.g., interview transcripts, survey responses, or other textual data) : The researcher reads through the entire data set (e.g., interview transcripts, survey responses, or field notes) multiple times to gain a comprehensive understanding of the data’s breadth and depth. This helps the researcher develop a holistic sense of the participants’ experiences, perspectives, and the overall narrative of the data.
• Listen to the audio recordings of the interviews : This helps to pick up on tone, emphasis, and emotional responses that may not be evident in the written transcripts. For instance, they might note a participant’s hesitation or excitement when discussing a particular topic. This is an important step if you didn’t collect the data or transcribe it yourself.
• Take notes on initial ideas and observations : Note-making at this stage should be observational and casual, not systematic and inclusive, as you aren’t coding yet. Think of the notes as memory aids and triggers for later coding and analysis. They are primarily for you, although they might be shared with research team members.
• Immerse yourself in the data to gain a deep understanding of its content : It’s not about just absorbing surface meaning like you would with a novel, but about thinking about what the data  mean .

By the end of the familiarization step, the researcher should have a good grasp of the overall content of the data, the key issues and experiences discussed by the participants, and any initial patterns or themes that emerge.

This deep engagement with the data sets the stage for the subsequent steps of thematic analysis, where the researcher will systematically code and analyze the data to identify and interpret the central themes.

Step 2: Generating Initial Codes

Codes are concise labels or descriptions assigned to segments of the data that capture a specific feature or meaning relevant to the research question.

The process of qualitative coding helps the researcher organize and reduce the data into manageable chunks, making it easier to identify patterns and themes relevant to the research question.

Think of it this way:  If your analysis is a house, themes are the walls and roof, while codes are the individual bricks and tiles.

Coding is an iterative process, with researchers refining and revising their codes as their understanding of the data evolves.

The ultimate goal is to develop a coherent and meaningful coding scheme that captures the richness and complexity of the participants’ experiences and helps answer the research questions.

Coding can be done manually (paper transcription and pen or highlighter) or by means of software (e.g. by using NVivo, MAXQDA or ATLAS.ti).

Decide On Your Coding Approach

• Will you use predefined deductive codes (based on theory or prior research), or let codes emerge from the data (inductive coding)?
• Will a piece of data have one code or multiple?
• Will you code everything or selectively? Broader research questions may warrant coding more comprehensively.

If you decide not to code everything, it’s crucial to:

• Have clear criteria for what you will and won’t code
• Be transparent about your selection process in research reports
• Remain open to revisiting uncoded data later in analysis

Do A First Round Of Coding

• Go through the data and assign initial codes to chunks that stand out
• Create a code name (a word or short phrase) that captures the essence of each chunk
• Keep a codebook – a list of your codes with descriptions or definitions
• Be open to adding, revising or combining codes as you go

After generating your first code, compare each new data extract to see if an existing code applies or a new one is needed.

Coding can be done at two levels of meaning:

• Semantic:  Provides a concise summary of a portion of data, staying close to the content and the participant’s meaning. For example, “Fear/anxiety about people’s reactions to his sexuality.”
• Latent:  Goes beyond the participant’s meaning to provide a conceptual interpretation of the data. For example, “Coming out imperative” interprets the meaning behind a participant’s statement.

Most codes will be a mix of descriptive and conceptual. Novice coders tend to generate more descriptive codes initially, developing more conceptual approaches with experience.

This step ends when:

• All data is fully coded.
• Data relevant to each code has been collated.

You have enough codes to capture the data’s diversity and patterns of meaning, with most codes appearing across multiple data items.

The number of codes you generate will depend on your topic, data set, and coding precision.

Step 3: Searching for Themes

Searching for themes begins after all data has been initially coded and collated, resulting in a comprehensive list of codes identified across the data set.

This step involves shifting from the specific, granular codes to a broader, more conceptual level of analysis.

Thematic analysis is not about “discovering” themes that already exist in the data, but rather actively constructing or generating themes through a careful and iterative process of examination and interpretation.

1 . Collating codes into potential themes :

The process of collating codes into potential themes involves grouping codes that share a unifying feature or represent a coherent and meaningful pattern in the data.

The researcher looks for patterns, similarities, and connections among the codes to develop overarching themes that capture the essence of the data.

By the end of this step, the researcher will have a collection of candidate themes and sub-themes, along with their associated data extracts.

However, these themes are still provisional and will be refined in the next step of reviewing the themes.

The searching for themes step helps the researcher move from a granular, code-level analysis to a more conceptual, theme-level understanding of the data.

This process is similar to sculpting, where the researcher shapes the “raw” data into a meaningful analysis.

This involves grouping codes that share a unifying feature or represent a coherent pattern in the data:

• Review the list of initial codes and their associated data extracts
• Look for codes that seem to share a common idea or concept
• Group related codes together to form potential themes
• Some codes may form main themes, while others may be sub-themes or may not fit into any theme

Thematic maps can help visualize the relationship between codes and themes. These visual aids provide a structured representation of the emerging patterns and connections within the data, aiding in understanding the significance of each theme and its contribution to the overall research question.

Example : Studying first-generation college students, the researcher might notice that the codes “financial challenges,” “working part-time,” and “scholarships” all relate to the broader theme of “Financial Obstacles and Support.”

Shared Meaning vs. Shared Topic in Thematic Analysis

Braun and Clarke distinguish between two different conceptualizations of  themes : topic summaries and shared meaning

• Topic summary themes , which they consider to be underdeveloped, are organized around a shared topic but not a shared meaning, and often resemble “buckets” into which data is sorted.
• Shared meaning themes  are patterns of shared meaning underpinned by a central organizing concept.

When grouping codes into themes, it’s crucial to ensure they share a central organizing concept or idea, reflecting a shared meaning rather than just belonging to the same topic.

Thematic analysis aims to uncover patterns of shared meaning within the data that offer insights into the research question

For example, codes centered around the concept of “Negotiating Sexual Identity” might not form one comprehensive theme, but rather two distinct themes: one related to “coming out and being out” and another exploring “different versions of being a gay man.”

Avoid : Themes as Topic Summaries (Shared Topic)

In this approach, themes simply summarize what participants mentioned about a particular topic, without necessarily revealing a unified meaning.

These themes are often underdeveloped and lack a central organizing concept.

It’s crucial to avoid creating themes that are merely summaries of data domains or directly reflect the interview questions. 

Example : A theme titled “Incidents of homophobia” that merely describes various participant responses about homophobia without delving into deeper interpretations would be a topic summary theme.

Tip : Using interview questions as theme titles without further interpretation or relying on generic social functions (“social conflict”) or structural elements (“economics”) as themes often indicates a lack of shared meaning and thorough theme development. Such themes might lack a clear connection to the specific dataset

Ensure : Themes as Shared Meaning

Instead, themes should represent a deeper level of interpretation, capturing the essence of the data and providing meaningful insights into the research question.

These themes go beyond summarizing a topic by identifying a central concept or idea that connects the codes.

They reflect a pattern of shared meaning across different data points, even if those points come from different topics.

Example : The theme “‘There’s always that level of uncertainty’: Compulsory heterosexuality at university” effectively captures the shared experience of fear and uncertainty among LGBT students, connecting various codes related to homophobia and its impact on their lives.

2. Gathering data relevant to each potential theme

Once a potential theme is identified, all coded data extracts associated with the codes grouped under that theme are collated. This ensures a comprehensive view of the data pertaining to each theme.

This involves reviewing the collated data extracts for each code and organizing them under the relevant themes.

For example, if you have a potential theme called “Student Strategies for Test Preparation,” you would gather all data extracts that have been coded with related codes, such as “Time Management for Test Preparation” or “Study Groups for Test Preparation”.

You can then begin reviewing the data extracts for each theme to see if they form a coherent pattern. 

This step helps to ensure that your themes accurately reflect the data and are not based on your own preconceptions.

It’s important to remember that coding is an organic and ongoing process.

You may need to re-read your entire data set to see if you have missed any data that is relevant to your themes, or if you need to create any new codes or themes.

The researcher should ensure that the data extracts within each theme are coherent and meaningful.

Example : The researcher would gather all the data extracts related to “Financial Obstacles and Support,” such as quotes about struggling to pay for tuition, working long hours, or receiving scholarships.

Here’s a more detailed explanation of how to gather data relevant to each potential theme:

• Start by creating a visual representation of your potential themes, such as a thematic map or table
• List each potential theme and its associated sub-themes (if any)
• This will help you organize your data and see the relationships between themes
• Go through your coded data extracts (e.g., highlighted quotes or segments from interview transcripts)
• For each coded extract, consider which theme or sub-theme it best fits under
• If a coded extract seems to fit under multiple themes, choose the theme that it most closely aligns with in terms of shared meaning
• As you identify which theme each coded extract belongs to, copy and paste the extract under the relevant theme in your thematic map or table
• Include enough context around each extract to ensure its meaning is clear
• If using qualitative data analysis software, you can assign the coded extracts to the relevant themes within the software
• As you gather data extracts under each theme, continuously review the extracts to ensure they form a coherent pattern
• If some extracts do not fit well with the rest of the data in a theme, consider whether they might better fit under a different theme or if the theme needs to be refined

3. Considering relationships between codes, themes, and different levels of themes

Once you have gathered all the relevant data extracts under each theme, review the themes to ensure they are meaningful and distinct.

This step involves analyzing how different codes combine to form overarching themes and exploring the hierarchical relationship between themes and sub-themes.

Within a theme, there can be different levels of themes, often organized hierarchically as main themes and sub-themes.

• Main themes  represent the most overarching or significant patterns found in the data. They provide a high-level understanding of the key issues or concepts present in the data. 
• Sub-themes , as the name suggests, fall under main themes, offering a more nuanced and detailed understanding of a particular aspect of the main theme.

The process of developing these relationships is iterative and involves:

• Creating a Thematic Map : The relationship between codes, sub-themes and main themes can be visualized using a thematic map, diagram, or table. Refine the thematic map as you continue to review and analyze the data.
• Examine how the codes and themes relate to each other : Some themes may be more prominent or overarching (main themes), while others may be secondary or subsidiary (sub-themes).
• Refining Themes : This map helps researchers review and refine themes, ensuring they are internally consistent (homogeneous) and distinct from other themes (heterogeneous).
• Defining and Naming Themes : Finally, themes are given clear and concise names and definitions that accurately reflect the meaning they represent in the data.

Consider how the themes tell a coherent story about the data and address the research question.

If some themes seem to overlap or are not well-supported by the data, consider combining or refining them.

If a theme is too broad or diverse, consider splitting it into separate themes or sub-theme.

Example : The researcher might identify “Academic Challenges” and “Social Adjustment” as other main themes, with sub-themes like “Imposter Syndrome” and “Balancing Work and School” under “Academic Challenges.” They would then consider how these themes relate to each other and contribute to the overall understanding of first-generation college students’ experiences.

Step 4: Reviewing Themes

The researcher reviews, modifies, and develops the preliminary themes identified in the previous step.

This phase involves a recursive process of checking the themes against the coded data extracts and the entire data set to ensure they accurately reflect the meanings evident in the data.

The purpose is to refine the themes, ensuring they are coherent, consistent, and distinctive.

According to Braun and Clarke, a well-developed theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set”.

A well-developed theme will:

• Go beyond paraphrasing the data to analyze the meaning and significance of the patterns identified.
• Provide a detailed analysis of what the theme is about.
• Be supported with a good amount of relevant data extracts.
• Be related to the research question.

Revisions at this stage might involve creating new themes, refining existing themes, or discarding themes that do not fit the data

Level One : Reviewing Themes Against Coded Data Extracts

• Researchers begin by comparing their candidate themes against the coded data extracts associated with each theme.
• This step helps to determine whether each theme is supported by the data and whether it accurately reflects the meaning found in the extracts. Determine if there is enough data to support each theme.
• Look at the relationships between themes and sub-themes in the thematic map. Consider whether the themes work together to tell a coherent story about the data. If the thematic map does not effectively represent the data, consider making adjustments to the themes or their organization.
• It’s important to ensure that each theme has a singular focus and is not trying to encompass too much. Themes should be distinct from one another, although they may build on or relate to each other.
• Discarding codes : If certain codes within a theme are not well-supported or do not fit, they can be removed.
• Relocating codes : Codes that fit better under a different theme can be moved.
• Redrawing theme boundaries : The scope of a theme can be adjusted to better capture the relevant data.
• Discarding themes : Entire themes can be abandoned if they do not work.

Level Two : Evaluating Themes Against the Entire Data Set

• Once the themes appear coherent and well-supported by the coded extracts, researchers move on to evaluate them against the entire data set.
• This involves a final review of all the data to ensure that the themes accurately capture the most important and relevant patterns across the entire dataset in relation to the research question.
• During this level, researchers may need to recode some extracts for consistency, especially if the coding process evolved significantly, and earlier data items were not recoded according to these changes.

Step 5: Defining and Naming Themes

The themes are finalized when the researcher is satisfied with the theme names and definitions.

If the analysis is carried out by a single researcher, it is recommended to seek feedback from an external expert to confirm that the themes are well-developed, clear, distinct, and capture all the relevant data.

Defining themes  means determining the exact meaning of each theme and understanding how it contributes to understanding the data.

This process involves formulating exactly what we mean by each theme. The researcher should consider what a theme says, if there are subthemes, how they interact and relate to the main theme, and how the themes relate to each other.

Themes should not be overly broad or try to encompass too much, and should have a singular focus. They should be distinct from one another and not repetitive, although they may build on one another.

In this phase the researcher specifies the essence of each theme.

• What does the theme tell us that is relevant for the research question?
• How does it fit into the ‘overall story’ the researcher wants to tell about the data?

Naming themes  involves developing a clear and concise name that effectively conveys the essence of each theme to the reader. A good name for a theme is informative, concise, and catchy.

• The researcher develops concise, punchy, and informative names for each theme that effectively communicate its essence to the reader.
• Theme names should be catchy and evocative, giving the reader an immediate sense of what the theme is about.
• Avoid using jargon or overly complex language in theme names.
• The name should go beyond simply paraphrasing the content of the data extracts and instead interpret the meaning and significance of the patterns within the theme.
• The goal is to make the themes accessible and easily understandable to the intended audience. If a theme contains sub-themes, the researcher should also develop clear and informative names for each sub-theme.
• Theme names can include direct quotations from the data, which helps convey the theme’s meaning. However, researchers should avoid using data collection questions as theme names. Using data collection questions as themes often leads to analyses that present summaries of topics rather than fully realized themes.

For example, “‘There’s always that level of uncertainty’: Compulsory heterosexuality at university” is a strong theme name because it captures the theme’s meaning. In contrast, “incidents of homophobia” is a weak theme name because it only states the topic.

For instance, a theme labeled “distrust of experts” might be renamed “distrust of authority” or “conspiracy thinking” after careful consideration of the theme’s meaning and scope.

Step 6: Producing the Report

A thematic analysis report should provide a convincing and clear, yet complex story about the data that is situated within a scholarly field.

A balance should be struck between the narrative and the data presented, ensuring that the report convincingly explains the meaning of the data, not just summarizes it.

To achieve this, the report should include vivid, compelling data extracts illustrating the themes and incorporate extracts from different data sources to demonstrate the themes’ prevalence and strengthen the analysis by representing various perspectives within the data.

The report should be written in first-person active tense, unless otherwise stated in the reporting requirements.

The analysis can be presented in two ways :

• Integrated Results and Discussion section:  This approach is suitable when the analysis has strong connections to existing research and when the analysis is more theoretical or interpretive.
• Separate Discussion section:  This approach presents the data interpretation separately from the results.

Regardless of the presentation style, researchers should aim to “show” what the data reveals and “tell” the reader what it means in order to create a convincing analysis.

• Presentation order of themes: Consider how to best structure the presentation of the themes in the report. This may involve presenting the themes in order of importance, chronologically, or in a way that tells a coherent story.
• Subheadings: Use subheadings to clearly delineate each theme and its sub-themes, making the report easy to navigate and understand.

The analysis should go beyond a simple summary of participant’s words and instead interpret the meaning of the data.

Themes should connect logically and meaningfully and, if relevant, should build on previous themes to tell a coherent story about the data.

The report should include vivid, compelling data extracts that clearly illustrate the theme being discussed and should incorporate extracts from different data sources, rather than relying on a single source.

Although it is tempting to rely on one source when it eloquently expresses a particular aspect of the theme, using multiple sources strengthens the analysis by representing a wider range of perspectives within the data.

Researchers should strive to maintain a balance between the amount of narrative and the amount of data presented.

Potential Pitfalls to Avoid

• Failing to analyze the data : Thematic analysis should involve more than simply presenting data extracts without an analytic narrative. The researcher must provide an interpretation and make sense of the data, telling the reader what it means and how it relates to the research questions.
• Using data collection questions as themes : Themes should be identified across the entire dataset, not just based on the questions asked during data collection. Reporting data collection questions as themes indicates a lack of thorough analytic work to identify patterns and meanings in the data.
• Conducting a weak or unconvincing analysis : Themes should be distinct, internally coherent, and consistent, capturing the majority of the data or providing a rich description of specific aspects. A weak analysis may have overlapping themes, fail to capture the data adequately, or lack sufficient examples to support the claims made.
• Mismatch between data and analytic claims : The researcher’s interpretations and analytic points must be consistent with the data extracts presented. Claims that are not supported by the data, contradict the data, or fail to consider alternative readings or variations in the account are problematic.
• Misalignment between theory, research questions, and analysis : The interpretations of the data should be consistent with the theoretical framework used. For example, an experiential framework would not typically make claims about the social construction of the topic. The form of thematic analysis used should also align with the research questions.
• Neglecting to clarify assumptions, purpose, and process : A good thematic analysis should spell out its theoretical assumptions, clarify how it was undertaken, and for what purpose. Without this crucial information, the analysis is lacking context and transparency, making it difficult for readers to evaluate the research.

Reducing Bias

When researchers are both reflexive and transparent in their thematic analysis, it strengthens the trustworthiness and rigor of their findings.

The explicit acknowledgement of potential biases and the detailed documentation of the analytical process provide a stronger foundation for the interpretation of the data, making it more likely that the findings reflect the perspectives of the participants rather than the biases of the researcher.

Reflexivity

Reflexivity involves critically examining one’s own assumptions and biases, is crucial in qualitative research to ensure the trustworthiness of findings.

It requires acknowledging that researcher subjectivity is inherent in the research process and can influence how data is collected, analyzed, and interpreted.

Identifying and Challenging Assumptions:

Reflexivity encourages researchers to explicitly acknowledge their preconceived notions, theoretical leanings, and potential biases.

By actively reflecting on how these factors might influence their interpretation of the data, researchers can take steps to mitigate their impact.

This might involve seeking alternative explanations, considering contradictory evidence, or discussing their interpretations with others to gain different perspectives.

Transparency

Transparency refers to clearly documenting the research process, including coding decisions, theme development, and the rationale behind behind theme development.

This openness allows others to understand how the analysis was conducted and to assess the credibility of the findings

This transparency helps ensure the trustworthiness and rigor of the findings, allowing others to understand and potentially replicate the analysis.

Documenting Decision-Making:

Transparency requires researchers to provide a clear and detailed account of their analytical choices throughout the research process.

This includes documenting the rationale behind coding decisions, the process of theme development, and any changes made to the analytical approach during the study.

By making these decisions transparent, researchers allow others to scrutinize their work and assess the potential for bias.

Practical Strategies for Reflexivity and Transparency in Thematic Analysis:

• Maintaining a reflexive journal:  Researchers can keep a journal throughout the research process to document their thoughts, assumptions, and potential biases. This journal serves as a record of the researcher’s evolving understanding of the data and can help identify potential blind spots in their analysis.
• Engaging in team-based analysis:  Collaborative analysis, involving multiple researchers, can enhance reflexivity by providing different perspectives and interpretations of the data. Discussing coding decisions and theme development as a team allows researchers to challenge each other’s assumptions and ensure a more comprehensive analysis.
• Clearly articulating the analytical process:  In reporting the findings of thematic analysis, researchers should provide a detailed account of their methods, including the rationale behind coding decisions, the process of theme development, and any challenges encountered during analysis. This transparency allows readers to understand the steps taken to ensure the rigor and trustworthiness of the analysis.
• Flexibility:  Thematic analysis is a flexible method, making it adaptable to different research questions and theoretical frameworks. It can be employed with various epistemological approaches, including realist, constructionist, and contextualist perspectives. For example, researchers can focus on analyzing meaning across the entire data set or examine a particular aspect in depth.
• Accessibility:  Thematic analysis is an accessible method, especially for novice qualitative researchers, as it doesn’t demand extensive theoretical or technical knowledge compared to methods like Discourse Analysis (DA) or Conversation Analysis (CA). It is considered a foundational qualitative analysis method.
• Rich Description:  Thematic analysis facilitates a rich and detailed description of data9. It can provide a thorough understanding of the predominant themes in a data set, offering valuable insights, particularly in under-researched areas.
• Theoretical Freedom:  Thematic analysis is not restricted to any pre-existing theoretical framework, allowing for diverse applications. This distinguishes it from methods like Grounded Theory or Interpretative Phenomenological Analysis (IPA), which are more closely tied to specific theoretical approaches

Disadvantages

• Subjectivity and Interpretation:  The flexibility of thematic analysis, while an advantage, can also be a disadvantage. The method’s openness can lead to a wide range of interpretations of the same data set, making it difficult to determine which aspects to emphasize. This potential subjectivity might raise concerns about the analysis’s reliability and consistency.
• Limited Interpretive Power:  Unlike methods like narrative analysis or biographical approaches, thematic analysis may not capture the nuances of individual experiences or contradictions within a single account. The focus on patterns across interviews could result in overlooking unique individual perspectives.
• Oversimplification:  Thematic analysis might oversimplify complex phenomena by focusing on common themes, potentially missing subtle but important variations within the data. If not carefully executed, the analysis may present a homogenous view of the data that doesn’t reflect the full range of perspectives.
• Lack of Established Theoretical Frameworks:  Thematic analysis does not inherently rely on pre-existing theoretical frameworks. While this allows for inductive exploration, it can also limit the interpretive power of the analysis if not anchored within a relevant theoretical context. The absence of a theoretical foundation might make it challenging to draw meaningful and generalizable conclusions.
• Difficulty in Higher-Phase Analysis:  While thematic analysis is relatively easy to initiate, the flexibility in its application can make it difficult to establish specific guidelines for higher-phase analysis1. Researchers may find it challenging to navigate the later stages of analysis and develop a coherent and insightful interpretation of the identified themes.
• Potential for Researcher Bias:  As with any qualitative research method, thematic analysis is susceptible to researcher bias. Researchers’ preconceived notions and assumptions can influence how they code and interpret data, potentially leading to skewed results.

Further Information

• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology, 3 (2), 77–101.
• Braun, V., & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. Sage.
• Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysi s. Qualitative Research in Sport, Exercise and Health, 11 (4), 589–597.
• Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18 (3), 328–352.
• Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales . Qualitative Research in Sport, Exercise and Health, 13 (2), 201–216.
• Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis .  Qualitative psychology ,  9 (1), 3.
• Braun, V., & Clarke, V. (2022b). Thematic analysis: A practical guide . Sage.
• Braun, V., Clarke, V., & Hayfield, N. (2022). ‘A starting point for your journey, not a map’: Nikki Hayfield in conversation with Virginia Braun and Victoria Clarke about thematic analysis.  Qualitative research in psychology ,  19 (2), 424-445.
• Finlay, L., & Gough, B. (Eds.). (2003). Reflexivity: A practical guide for researchers in health and social sciences. Blackwell Science.
• Gibbs, G. R. (2013). Using software in qualitative analysis. In U. Flick (ed.) The Sage handbook of qualitative data analysis (pp. 277–294). London: Sage.
• Terry, G., & Hayfield, N. (2021). Essentials of thematic analysis . American Psychological Association.

Example TA Studies

• Braun, V., Terry, G., Gavey, N., & Fenaughty, J. (2009). ‘ Risk’and sexual coercion among gay and bisexual men in Aotearoa/New Zealand–key informant accounts .  Culture, Health & Sexuality ,  11 (2), 111-124.
• Clarke, V., & Kitzinger, C. (2004). Lesbian and gay parents on talk shows: resistance or collusion in heterosexism? .  Qualitative Research in Psychology ,  1 (3), 195-217.

• Research article
• Open access
• Published: 10 July 2008

Methods for the thematic synthesis of qualitative research in systematic reviews

• James Thomas 1 &
• Angela Harden 1  

BMC Medical Research Methodology volume  8 , Article number:  45 ( 2008 ) Cite this article

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There is a growing recognition of the value of synthesising qualitative research in the evidence base in order to facilitate effective and appropriate health care. In response to this, methods for undertaking these syntheses are currently being developed. Thematic analysis is a method that is often used to analyse data in primary qualitative research. This paper reports on the use of this type of analysis in systematic reviews to bring together and integrate the findings of multiple qualitative studies.

We describe thematic synthesis, outline several steps for its conduct and illustrate the process and outcome of this approach using a completed review of health promotion research. Thematic synthesis has three stages: the coding of text 'line-by-line'; the development of 'descriptive themes'; and the generation of 'analytical themes'. While the development of descriptive themes remains 'close' to the primary studies, the analytical themes represent a stage of interpretation whereby the reviewers 'go beyond' the primary studies and generate new interpretive constructs, explanations or hypotheses. The use of computer software can facilitate this method of synthesis; detailed guidance is given on how this can be achieved.

We used thematic synthesis to combine the studies of children's views and identified key themes to explore in the intervention studies. Most interventions were based in school and often combined learning about health benefits with 'hands-on' experience. The studies of children's views suggested that fruit and vegetables should be treated in different ways, and that messages should not focus on health warnings. Interventions that were in line with these suggestions tended to be more effective. Thematic synthesis enabled us to stay 'close' to the results of the primary studies, synthesising them in a transparent way, and facilitating the explicit production of new concepts and hypotheses.

We compare thematic synthesis to other methods for the synthesis of qualitative research, discussing issues of context and rigour. Thematic synthesis is presented as a tried and tested method that preserves an explicit and transparent link between conclusions and the text of primary studies; as such it preserves principles that have traditionally been important to systematic reviewing.

Peer Review reports

The systematic review is an important technology for the evidence-informed policy and practice movement, which aims to bring research closer to decision-making [ 1 , 2 ]. This type of review uses rigorous and explicit methods to bring together the results of primary research in order to provide reliable answers to particular questions [ 3 – 6 ]. The picture that is presented aims to be distorted neither by biases in the review process nor by biases in the primary research which the review contains [ 7 – 10 ]. Systematic review methods are well-developed for certain types of research, such as randomised controlled trials (RCTs). Methods for reviewing qualitative research in a systematic way are still emerging, and there is much ongoing development and debate [ 11 – 14 ].

In this paper we present one approach to the synthesis of findings of qualitative research, which we have called 'thematic synthesis'. We have developed and applied these methods within several systematic reviews that address questions about people's perspectives and experiences [ 15 – 18 ]. The context for this methodological development is a programme of work in health promotion and public health (HP & PH), mostly funded by the English Department of Health, at the EPPI-Centre, in the Social Science Research Unit at the Institute of Education, University of London in the UK. Early systematic reviews at the EPPI-Centre addressed the question 'what works?' and contained research testing the effects of interventions. However, policy makers and other review users also posed questions about intervention need, appropriateness and acceptability, and factors influencing intervention implementation. To address these questions, our reviews began to include a wider range of research, including research often described as 'qualitative'. We began to focus, in particular, on research that aimed to understand the health issue in question from the experiences and point of view of the groups of people targeted by HP&PH interventions (We use the term 'qualitative' research cautiously because it encompasses a multitude of research methods at the same time as an assumed range of epistemological positions. In practice it is often difficult to classify research as being either 'qualitative' or 'quantitative' as much research contains aspects of both [ 19 – 22 ]. Because the term is in common use, however, we will employ it in this paper).

When we started the work for our first series of reviews which included qualitative research in 1999 [ 23 – 26 ], there was very little published material that described methods for synthesising this type of research. We therefore experimented with a variety of techniques borrowed from standard systematic review methods and methods for analysing primary qualitative research [ 15 ]. In later reviews, we were able to refine these methods and began to apply thematic analysis in a more explicit way. The methods for thematic synthesis described in this paper have so far been used explicitly in three systematic reviews [ 16 – 18 ].

The review used as an example in this paper

To illustrate the steps involved in a thematic synthesis we draw on a review of the barriers to, and facilitators of, healthy eating amongst children aged four to 10 years old [ 17 ]. The review was commissioned by the Department of Health, England to inform policy about how to encourage children to eat healthily in the light of recent surveys highlighting that British children are eating less than half the recommended five portions of fruit and vegetables per day. While we focus on the aspects of the review that relate to qualitative studies, the review was broader than this and combined answering traditional questions of effectiveness, through reviewing controlled trials, with questions relating to children's views of healthy eating, which were answered using qualitative studies. The qualitative studies were synthesised using 'thematic synthesis' – the subject of this paper. We compared the effectiveness of interventions which appeared to be in line with recommendations from the thematic synthesis with those that did not. This enabled us to see whether the understandings we had gained from the children's views helped us to explain differences in the effectiveness of different interventions: the thematic synthesis had enabled us to generate hypotheses which could be tested against the findings of the quantitative studies – hypotheses that we could not have generated without the thematic synthesis. The methods of this part of the review are published in Thomas et al . [ 27 ] and are discussed further in Harden and Thomas [ 21 ].

Qualitative research and systematic reviews

The act of seeking to synthesise qualitative research means stepping into more complex and contested territory than is the case when only RCTs are included in a review. First, methods are much less developed in this area, with fewer completed reviews available from which to learn, and second, the whole enterprise of synthesising qualitative research is itself hotly debated. Qualitative research, it is often proposed, is not generalisable and is specific to a particular context, time and group of participants. Thus, in bringing such research together, reviewers are open to the charge that they de-contextualise findings and wrongly assume that these are commensurable [ 11 , 13 ]. These are serious concerns which it is not the purpose of this paper to contest. We note, however, that a strong case has been made for qualitative research to be valued for the potential it has to inform policy and practice [ 11 , 28 – 30 ]. In our experience, users of reviews are interested in the answers that only qualitative research can provide, but are not able to handle the deluge of data that would result if they tried to locate, read and interpret all the relevant research themselves. Thus, if we acknowledge the unique importance of qualitative research, we need also to recognise that methods are required to bring its findings together for a wide audience – at the same time as preserving and respecting its essential context and complexity.

The earliest published work that we know of that deals with methods for synthesising qualitative research was written in 1988 by Noblit and Hare [ 31 ]. This book describes the way that ethnographic research might be synthesised, but the method has been shown to be applicable to qualitative research beyond ethnography [ 32 , 11 ]. As well as meta-ethnography, other methods have been developed more recently, including 'meta-study' [ 33 ], 'critical interpretive synthesis' [ 34 ] and 'metasynthesis' [ 13 ].

Many of the newer methods being developed have much in common with meta-ethnography, as originally described by Noblit and Hare, and often state explicitly that they are drawing on this work. In essence, this method involves identifying key concepts from studies and translating them into one another. The term 'translating' in this context refers to the process of taking concepts from one study and recognising the same concepts in another study, though they may not be expressed using identical words. Explanations or theories associated with these concepts are also extracted and a 'line of argument' may be developed, pulling corroborating concepts together and, crucially, going beyond the content of the original studies (though 'refutational' concepts might not be amenable to this process). Some have claimed that this notion of 'going beyond' the primary studies is a critical component of synthesis, and is what distinguishes it from the types of summaries of findings that typify traditional literature reviews [e.g. [ 32 ], p209]. In the words of Margarete Sandelowski, "metasyntheses are integrations that are more than the sum of parts, in that they offer novel interpretations of findings. These interpretations will not be found in any one research report but, rather, are inferences derived from taking all of the reports in a sample as a whole" [[ 14 ], p1358].

Thematic analysis has been identified as one of a range of potential methods for research synthesis alongside meta-ethnography and 'metasynthesis', though precisely what the method involves is unclear, and there are few examples of it being used for synthesising research [ 35 ]. We have adopted the term 'thematic synthesis', as we translated methods for the analysis of primary research – often termed 'thematic' – for use in systematic reviews [ 36 – 38 ]. As Boyatzis [[ 36 ], p4] has observed, thematic analysis is "not another qualitative method but a process that can be used with most, if not all, qualitative methods..." . Our approach concurs with this conceptualisation of thematic analysis, since the method we employed draws on other established methods but uses techniques commonly described as 'thematic analysis' in order to formalise the identification and development of themes.

We now move to a description of the methods we used in our example systematic review. While this paper has the traditional structure for reporting the results of a research project, the detailed methods (e.g. precise terms we used for searching) and results are available online. This paper identifies the particular issues that relate especially to reviewing qualitative research systematically and then to describing the activity of thematic synthesis in detail.

When searching for studies for inclusion in a 'traditional' statistical meta-analysis, the aim of searching is to locate all relevant studies. Failing to do this can undermine the statistical models that underpin the analysis and bias the results. However, Doyle [[ 39 ], p326] states that, "like meta-analysis, meta-ethnography utilizes multiple empirical studies but, unlike meta-analysis, the sample is purposive rather than exhaustive because the purpose is interpretive explanation and not prediction" . This suggests that it may not be necessary to locate every available study because, for example, the results of a conceptual synthesis will not change if ten rather than five studies contain the same concept, but will depend on the range of concepts found in the studies, their context, and whether they are in agreement or not. Thus, principles such as aiming for 'conceptual saturation' might be more appropriate when planning a search strategy for qualitative research, although it is not yet clear how these principles can be applied in practice. Similarly, other principles from primary qualitative research methods may also be 'borrowed' such as deliberately seeking studies which might act as negative cases, aiming for maximum variability and, in essence, designing the resulting set of studies to be heterogeneous, in some ways, instead of achieving the homogeneity that is often the aim in statistical meta-analyses.

However you look, qualitative research is difficult to find [ 40 – 42 ]. In our review, it was not possible to rely on simple electronic searches of databases. We needed to search extensively in 'grey' literature, ask authors of relevant papers if they knew of more studies, and look especially for book chapters, and we spent a lot of effort screening titles and abstracts by hand and looking through journals manually. In this sense, while we were not driven by the statistical imperative of locating every relevant study, when it actually came down to searching, we found that there was very little difference in the methods we had to use to find qualitative studies compared to the methods we use when searching for studies for inclusion in a meta-analysis.

Quality assessment

Assessing the quality of qualitative research has attracted much debate and there is little consensus regarding how quality should be assessed, who should assess quality, and, indeed, whether quality can or should be assessed in relation to 'qualitative' research at all [ 43 , 22 , 44 , 45 ]. We take the view that the quality of qualitative research should be assessed to avoid drawing unreliable conclusions. However, since there is little empirical evidence on which to base decisions for excluding studies based on quality assessment, we took the approach in this review to use 'sensitivity analyses' (described below) to assess the possible impact of study quality on the review's findings.

In our example review we assessed our studies according to 12 criteria, which were derived from existing sets of criteria proposed for assessing the quality of qualitative research [ 46 – 49 ], principles of good practice for conducting social research with children [ 50 ], and whether studies employed appropriate methods for addressing our review questions. The 12 criteria covered three main quality issues. Five related to the quality of the reporting of a study's aims, context, rationale, methods and findings (e.g. was there an adequate description of the sample used and the methods for how the sample was selected and recruited?). A further four criteria related to the sufficiency of the strategies employed to establish the reliability and validity of data collection tools and methods of analysis, and hence the validity of the findings. The final three criteria related to the assessment of the appropriateness of the study methods for ensuring that findings about the barriers to, and facilitators of, healthy eating were rooted in children's own perspectives (e.g. were data collection methods appropriate for helping children to express their views?).

Extracting data from studies

One issue which is difficult to deal with when synthesising 'qualitative' studies is 'what counts as data' or 'findings'? This problem is easily addressed when a statistical meta-analysis is being conducted: the numeric results of RCTs – for example, the mean difference in outcome between the intervention and control – are taken from published reports and are entered into the software package being used to calculate the pooled effect size [ 3 , 51 ].

Deciding what to abstract from the published report of a 'qualitative' study is much more difficult. Campbell et al . [ 11 ] extracted what they called the 'key concepts' from the qualitative studies they found about patients' experiences of diabetes and diabetes care. However, finding the key concepts in 'qualitative' research is not always straightforward either. As Sandelowski and Barroso [ 52 ] discovered, identifying the findings in qualitative research can be complicated by varied reporting styles or the misrepresentation of data as findings (as for example when data are used to 'let participants speak for themselves'). Sandelowski and Barroso [ 53 ] have argued that the findings of qualitative (and, indeed, all empirical) research are distinct from the data upon which they are based, the methods used to derive them, externally sourced data, and researchers' conclusions and implications.

In our example review, while it was relatively easy to identify 'data' in the studies – usually in the form of quotations from the children themselves – it was often difficult to identify key concepts or succinct summaries of findings, especially for studies that had undertaken relatively simple analyses and had not gone much further than describing and summarising what the children had said. To resolve this problem we took study findings to be all of the text labelled as 'results' or 'findings' in study reports – though we also found 'findings' in the abstracts which were not always reported in the same way in the text. Study reports ranged in size from a few pages to full final project reports. We entered all the results of the studies verbatim into QSR's NVivo software for qualitative data analysis. Where we had the documents in electronic form this process was straightforward even for large amounts of text. When electronic versions were not available, the results sections were either re-typed or scanned in using a flat-bed or pen scanner. (We have since adapted our own reviewing system, 'EPPI-Reviewer' [ 54 ], to handle this type of synthesis and the screenshots below show this software.)

Detailed methods for thematic synthesis

The synthesis took the form of three stages which overlapped to some degree: the free line-by-line coding of the findings of primary studies; the organisation of these 'free codes' into related areas to construct 'descriptive' themes; and the development of 'analytical' themes.

Stages one and two: coding text and developing descriptive themes

In our children and healthy eating review, we originally planned to extract and synthesise study findings according to our review questions regarding the barriers to, and facilitators of, healthy eating amongst children. It soon became apparent, however, that few study findings addressed these questions directly and it appeared that we were in danger of ending up with an empty synthesis. We were also concerned about imposing the a priori framework implied by our review questions onto study findings without allowing for the possibility that a different or modified framework may be a better fit. We therefore temporarily put our review questions to one side and started from the study findings themselves to conduct an thematic analysis.

There were eight relevant qualitative studies examining children's views of healthy eating. We entered the verbatim findings of these studies into our database. Three reviewers then independently coded each line of text according to its meaning and content. Figure 1 illustrates this line-by-line coding using our specialist reviewing software, EPPI-Reviewer, which includes a component designed to support thematic synthesis. The text which was taken from the report of the primary study is on the left and codes were created inductively to capture the meaning and content of each sentence. Codes could be structured, either in a tree form (as shown in the figure) or as 'free' codes – without a hierarchical structure.

line-by-line coding in EPPI-Reviewer.

The use of line-by-line coding enabled us to undertake what has been described as one of the key tasks in the synthesis of qualitative research: the translation of concepts from one study to another [ 32 , 55 ]. However, this process may not be regarded as a simple one of translation. As we coded each new study we added to our 'bank' of codes and developed new ones when necessary. As well as translating concepts between studies, we had already begun the process of synthesis (For another account of this process, see Doyle [[ 39 ], p331]). Every sentence had at least one code applied, and most were categorised using several codes (e.g. 'children prefer fruit to vegetables' or 'why eat healthily?'). Before completing this stage of the synthesis, we also examined all the text which had a given code applied to check consistency of interpretation and to see whether additional levels of coding were needed. (In grounded theory this is termed 'axial' coding; see Fisher [ 55 ] for further discussion of the application of axial coding in research synthesis.) This process created a total of 36 initial codes. For example, some of the text we coded as "bad food = nice, good food = awful" from one study [ 56 ] were:

'All the things that are bad for you are nice and all the things that are good for you are awful.' (Boys, year 6) [[ 56 ], p74]

'All adverts for healthy stuff go on about healthy things. The adverts for unhealthy things tell you how nice they taste.' [[ 56 ], p75]

Some children reported throwing away foods they knew had been put in because they were 'good for you' and only ate the crisps and chocolate . [[ 56 ], p75]

Reviewers looked for similarities and differences between the codes in order to start grouping them into a hierarchical tree structure. New codes were created to capture the meaning of groups of initial codes. This process resulted in a tree structure with several layers to organize a total of 12 descriptive themes (Figure 2 ). For example, the first layer divided the 12 themes into whether they were concerned with children's understandings of healthy eating or influences on children's food choice. The above example, about children's preferences for food, was placed in both areas, since the findings related both to children's reactions to the foods they were given, and to how they behaved when given the choice over what foods they might eat. A draft summary of the findings across the studies organized by the 12 descriptive themes was then written by one of the review authors. Two other review authors commented on this draft and a final version was agreed.

relationships between descriptive themes.

Stage three: generating analytical themes

Up until this point, we had produced a synthesis which kept very close to the original findings of the included studies. The findings of each study had been combined into a whole via a listing of themes which described children's perspectives on healthy eating. However, we did not yet have a synthesis product that addressed directly the concerns of our review – regarding how to promote healthy eating, in particular fruit and vegetable intake, amongst children. Neither had we 'gone beyond' the findings of the primary studies and generated additional concepts, understandings or hypotheses. As noted earlier, the idea or step of 'going beyond' the content of the original studies has been identified by some as the defining characteristic of synthesis [ 32 , 14 ].

This stage of a qualitative synthesis is the most difficult to describe and is, potentially, the most controversial, since it is dependent on the judgement and insights of the reviewers. The equivalent stage in meta-ethnography is the development of 'third order interpretations' which go beyond the content of original studies [ 32 , 11 ]. In our example, the step of 'going beyond' the content of the original studies was achieved by using the descriptive themes that emerged from our inductive analysis of study findings to answer the review questions we had temporarily put to one side. Reviewers inferred barriers and facilitators from the views children were expressing about healthy eating or food in general, captured by the descriptive themes, and then considered the implications of children's views for intervention development. Each reviewer first did this independently and then as a group. Through this discussion more abstract or analytical themes began to emerge. The barriers and facilitators and implications for intervention development were examined again in light of these themes and changes made as necessary. This cyclical process was repeated until the new themes were sufficiently abstract to describe and/or explain all of our initial descriptive themes, our inferred barriers and facilitators and implications for intervention development.

For example, five of the 12 descriptive themes concerned the influences on children's choice of foods (food preferences, perceptions of health benefits, knowledge behaviour gap, roles and responsibilities, non-influencing factors). From these, reviewers inferred several barriers and implications for intervention development. Children identified readily that taste was the major concern for them when selecting food and that health was either a secondary factor or, in some cases, a reason for rejecting food. Children also felt that buying healthy food was not a legitimate use of their pocket money, which they would use to buy sweets that could be enjoyed with friends. These perspectives indicated to us that branding fruit and vegetables as a 'tasty' rather than 'healthy' might be more effective in increasing consumption. As one child noted astutely, 'All adverts for healthy stuff go on about healthy things. The adverts for unhealthy things tell you how nice they taste.' [[ 56 ], p75]. We captured this line of argument in the analytical theme entitled 'Children do not see it as their role to be interested in health'. Altogether, this process resulted in the generation of six analytical themes which were associated with ten recommendations for interventions.

Six main issues emerged from the studies of children's views: (1) children do not see it as their role to be interested in health; (2) children do not see messages about future health as personally relevant or credible; (3) fruit, vegetables and confectionery have very different meanings for children; (4) children actively seek ways to exercise their own choices with regard to food; (5) children value eating as a social occasion; and (6) children see the contradiction between what is promoted in theory and what adults provide in practice. The review found that most interventions were based in school (though frequently with parental involvement) and often combined learning about the health benefits of fruit and vegetables with 'hands-on' experience in the form of food preparation and taste-testing. Interventions targeted at people with particular risk factors worked better than others, and multi-component interventions that combined the promotion of physical activity with healthy eating did not work as well as those that only concentrated on healthy eating. The studies of children's views suggested that fruit and vegetables should be treated in different ways in interventions, and that messages should not focus on health warnings. Interventions that were in line with these suggestions tended to be more effective than those which were not.

Context and rigour in thematic synthesis

The process of translation, through the development of descriptive and analytical themes, can be carried out in a rigorous way that facilitates transparency of reporting. Since we aim to produce a synthesis that both generates 'abstract and formal theories' that are nevertheless 'empirically faithful to the cases from which they were developed' [[ 53 ], p1371], we see the explicit recording of the development of themes as being central to the method. The use of software as described can facilitate this by allowing reviewers to examine the contribution made to their findings by individual studies, groups of studies, or sub-populations within studies.

Some may argue against the synthesis of qualitative research on the grounds that the findings of individual studies are de-contextualised and that concepts identified in one setting are not applicable to others [ 32 ]. However, the act of synthesis could be viewed as similar to the role of a research user when reading a piece of qualitative research and deciding how useful it is to their own situation. In the case of synthesis, reviewers translate themes and concepts from one situation to another and can always be checking that each transfer is valid and whether there are any reasons that understandings gained in one context might not be transferred to another. We attempted to preserve context by providing structured summaries of each study detailing aims, methods and methodological quality, and setting and sample. This meant that readers of our review were able to judge for themselves whether or not the contexts of the studies the review contained were similar to their own. In the synthesis we also checked whether the emerging findings really were transferable across different study contexts. For example, we tried throughout the synthesis to distinguish between participants (e.g. boys and girls) where the primary research had made an appropriate distinction. We then looked to see whether some of our synthesis findings could be attributed to a particular group of children or setting. In the event, we did not find any themes that belonged to a specific group, but another outcome of this process was a realisation that the contextual information given in the reports of studies was very restricted indeed. It was therefore difficult to make the best use of context in our synthesis.

In checking that we were not translating concepts into situations where they did not belong, we were following a principle that others have followed when using synthesis methods to build grounded formal theory: that of grounding a text in the context in which it was constructed. As Margaret Kearney has noted "the conditions under which data were collected, analysis was done, findings were found, and products were written for each contributing report should be taken into consideration in developing a more generalized and abstract model" [[ 14 ], p1353]. Britten et al . [ 32 ] suggest that it may be important to make a deliberate attempt to include studies conducted across diverse settings to achieve the higher level of abstraction that is aimed for in a meta-ethnography.

Study quality and sensitivity analyses

We assessed the 'quality' of our studies with regard to the degree to which they represented the views of their participants. In doing this, we were locating the concept of 'quality' within the context of the purpose of our review – children's views – and not necessarily the context of the primary studies themselves. Our 'hierarchy of evidence', therefore, did not prioritise the research design of studies but emphasised the ability of the studies to answer our review question. A traditional systematic review of controlled trials would contain a quality assessment stage, the purpose of which is to exclude studies that do not provide a reliable answer to the review question. However, given that there were no accepted – or empirically tested – methods for excluding qualitative studies from syntheses on the basis of their quality [ 57 , 12 , 58 ], we included all studies regardless of their quality.

Nevertheless, our studies did differ according to the quality criteria they were assessed against and it was important that we considered this in some way. In systematic reviews of trials, 'sensitivity analyses' – analyses which test the effect on the synthesis of including and excluding findings from studies of differing quality – are often carried out. Dixon-Woods et al . [ 12 ] suggest that assessing the feasibility and worth of conducting sensitivity analyses within syntheses of qualitative research should be an important focus of synthesis methods work. After our thematic synthesis was complete, we examined the relative contributions of studies to our final analytic themes and recommendations for interventions. We found that the poorer quality studies contributed comparatively little to the synthesis and did not contain many unique themes; the better studies, on the other hand, appeared to have more developed analyses and contributed most to the synthesis.

This paper has discussed the rationale for reviewing and synthesising qualitative research in a systematic way and has outlined one specific approach for doing this: thematic synthesis. While it is not the only method which might be used – and we have discussed some of the other options available – we present it here as a tested technique that has worked in the systematic reviews in which it has been employed.

We have observed that one of the key tasks in the synthesis of qualitative research is the translation of concepts between studies. While the activity of translating concepts is usually undertaken in the few syntheses of qualitative research that exist, there are few examples that specify the detail of how this translation is actually carried out. The example above shows how we achieved the translation of concepts across studies through the use of line-by-line coding, the organisation of these codes into descriptive themes, and the generation of analytical themes through the application of a higher level theoretical framework. This paper therefore also demonstrates how the methods and process of a thematic synthesis can be written up in a transparent way.

This paper goes some way to addressing concerns regarding the use of thematic analysis in research synthesis raised by Dixon-Woods and colleagues who argue that the approach can lack transparency due to a failure to distinguish between 'data-driven' or 'theory-driven' approaches. Moreover they suggest that, "if thematic analysis is limited to summarising themes reported in primary studies, it offers little by way of theoretical structure within which to develop higher order thematic categories..." [[ 35 ], p47]. Part of the problem, they observe, is that the precise methods of thematic synthesis are unclear. Our approach contains a clear separation between the 'data-driven' descriptive themes and the 'theory-driven' analytical themes and demonstrates how the review questions provided a theoretical structure within which it became possible to develop higher order thematic categories.

The theme of 'going beyond' the content of the primary studies was discussed earlier. Citing Strike and Posner [ 59 ], Campbell et al . [[ 11 ], p672] also suggest that synthesis "involves some degree of conceptual innovation, or employment of concepts not found in the characterisation of the parts and a means of creating the whole" . This was certainly true of the example given in this paper. We used a series of questions, derived from the main topic of our review, to focus an examination of our descriptive themes and we do not find our recommendations for interventions contained in the findings of the primary studies: these were new propositions generated by the reviewers in the light of the synthesis. The method also demonstrates that it is possible to synthesise without conceptual innovation. The initial synthesis, involving the translation of concepts between studies, was necessary in order for conceptual innovation to begin. One could argue that the conceptual innovation, in this case, was only necessary because the primary studies did not address our review question directly. In situations in which the primary studies are concerned directly with the review question, it may not be necessary to go beyond the contents of the original studies in order to produce a satisfactory synthesis (see, for example, Marston and King, [ 60 ]). Conceptually, our analytical themes are similar to the ultimate product of meta-ethnographies: third order interpretations [ 11 ], since both are explicit mechanisms for going beyond the content of the primary studies and presenting this in a transparent way. The main difference between them lies in their purposes. Third order interpretations bring together the implications of translating studies into one another in their own terms, whereas analytical themes are the result of interrogating a descriptive synthesis by placing it within an external theoretical framework (our review question and sub-questions). It may be, therefore, that analytical themes are more appropriate when a specific review question is being addressed (as often occurs when informing policy and practice), and third order interpretations should be used when a body of literature is being explored in and of itself, with broader, or emergent, review questions.

This paper is a contribution to the current developmental work taking place in understanding how best to bring together the findings of qualitative research to inform policy and practice. It is by no means the only method on offer but, by drawing on methods and principles from qualitative primary research, it benefits from the years of methodological development that underpins the research it seeks to synthesise.

Chalmers I: Trying to do more good than harm in policy and practice: the role of rigorous, transparent and up-to-date evaluations. Ann Am Acad Pol Soc Sci. 2003, 589: 22-40. 10.1177/0002716203254762.

Article   Google Scholar  

Oakley A: Social science and evidence-based everything: the case of education. Educ Rev. 2002, 54: 277-286. 10.1080/0013191022000016329.

Cooper H, Hedges L: The Handbook of Research Synthesis. 1994, New York: Russell Sage Foundation

Google Scholar  

EPPI-Centre: EPPI-Centre Methods for Conducting Systematic Reviews. 2006, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=89 ]

Higgins J, Green S, (Eds): Cochrane Handbook for Systematic Reviews of Interventions 4.2.6. 2006, Updated September 2006. Accessed 24th January 2007, [ http://www.cochrane.org/resources/handbook/ ]

Petticrew M, Roberts H: Systematic Reviews in the Social Sciences: A practical guide. 2006, Oxford: Blackwell Publishing

Book   Google Scholar  

Chalmers I, Hedges L, Cooper H: A brief history of research synthesis. Eval Health Prof. 2002, 25: 12-37. 10.1177/0163278702025001003.

Article   PubMed   Google Scholar  

Juni P, Altman D, Egger M: Assessing the quality of controlled clinical trials. BMJ. 2001, 323: 42-46. 10.1136/bmj.323.7303.42.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Mulrow C: Systematic reviews: rationale for systematic reviews. BMJ. 1994, 309: 597-599.

White H: Scientific communication and literature retrieval. The Handbook of Research Synthesis. Edited by: Cooper H, Hedges L. 1994, New York: Russell Sage Foundation

Campbell R, Pound P, Pope C, Britten N, Pill R, Morgan M, Donovan J: Evaluating meta-ethnography: a synthesis of qualitative research on lay experiences of diabetes and diabetes care. Soc Sci Med. 2003, 56: 671-684. 10.1016/S0277-9536(02)00064-3.

Dixon-Woods M, Bonas S, Booth A, Jones DR, Miller T, Sutton AJ, Shaw RL, Smith JA, Young B: How can systematic reviews incorporate qualitative research? A critical perspective. Qual Res. 2006, 6: 27-44. 10.1177/1468794106058867.

Sandelowski M, Barroso J: Handbook for Synthesising Qualitative Research. 2007, New York: Springer

Thorne S, Jensen L, Kearney MH, Noblit G, Sandelowski M: Qualitative meta-synthesis: reflections on methodological orientation and ideological agenda. Qual Health Res. 2004, 14: 1342-1365. 10.1177/1049732304269888.

Harden A, Garcia J, Oliver S, Rees R, Shepherd J, Brunton G, Oakley A: Applying systematic review methods to studies of people's views: an example from public health. J Epidemiol Community Health. 2004, 58: 794-800. 10.1136/jech.2003.014829.

Article   PubMed   PubMed Central   Google Scholar  

Harden A, Brunton G, Fletcher A, Oakley A: Young People, Pregnancy and Social Exclusion: A systematic synthesis of research evidence to identify effective, appropriate and promising approaches for prevention and support. 2006, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=674 ]

Thomas J, Sutcliffe K, Harden A, Oakley A, Oliver S, Rees R, Brunton G, Kavanagh J: Children and Healthy Eating: A systematic review of barriers and facilitators. 2003, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, accessed 4 th July 2008, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=246 ]

Thomas J, Kavanagh J, Tucker H, Burchett H, Tripney J, Oakley A: Accidental Injury, Risk-Taking Behaviour and the Social Circumstances in which Young People Live: A systematic review. 2007, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=1910 ]

Bryman A: Quantity and Quality in Social Research. 1998, London: Unwin

Hammersley M: What's Wrong with Ethnography?. 1992, London: Routledge

Harden A, Thomas J: Methodological issues in combining diverse study types in systematic reviews. Int J Soc Res Meth. 2005, 8: 257-271. 10.1080/13645570500155078.

Oakley A: Experiments in Knowing: Gender and methods in the social sciences. 2000, Cambridge: Polity Press

Harden A, Oakley A, Oliver S: Peer-delivered health promotion for young people: a systematic review of different study designs. Health Educ J. 2001, 60: 339-353. 10.1177/001789690106000406.

Harden A, Rees R, Shepherd J, Brunton G, Oliver S, Oakley A: Young People and Mental Health: A systematic review of barriers and facilitators. 2001, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=256 ]

Rees R, Harden A, Shepherd J, Brunton G, Oliver S, Oakley A: Young People and Physical Activity: A systematic review of barriers and facilitators. 2001, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=260 ]

Shepherd J, Harden A, Rees R, Brunton G, Oliver S, Oakley A: Young People and Healthy Eating: A systematic review of barriers and facilitators. 2001, London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, [ http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=258 ]

Thomas J, Harden A, Oakley A, Oliver S, Sutcliffe K, Rees R, Brunton G, Kavanagh J: Integrating qualitative research with trials in systematic reviews: an example from public health. BMJ. 2004, 328: 1010-1012. 10.1136/bmj.328.7446.1010.

Davies P: What is evidence-based education?. Br J Educ Stud. 1999, 47: 108-121. 10.1111/1467-8527.00106.

Newman M, Thompson C, Roberts AP: Helping practitioners understand the contribution of qualitative research to evidence-based practice. Evid Based Nurs. 2006, 9: 4-7. 10.1136/ebn.9.1.4.

Popay J: Moving Beyond Effectiveness in Evidence Synthesis. 2006, London: National Institute for Health and Clinical Excellence

Noblit GW, Hare RD: Meta-Ethnography: Synthesizing qualitative studies. 1988, Newbury Park: Sage

Britten N, Campbell R, Pope C, Donovan J, Morgan M, Pill R: Using meta-ethnography to synthesise qualitative research: a worked example. J Health Serv Res Policy. 2002, 7: 209-215. 10.1258/135581902320432732.

Paterson B, Thorne S, Canam C, Jillings C: Meta-Study of Qualitative Health Research. 2001, Thousand Oaks, California: Sage

Dixon-Woods M, Cavers D, Agarwal S, Annandale E, Arthur A, Harvey J, Katbamna S, Olsen R, Smith L, Riley R, Sutton AJ: Conducting a critical interpretative synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Methodol. 2006, 6: 35-10.1186/1471-2288-6-35.

Dixon-Woods M, Agarwal S, Jones D, Young B, Sutton A: Synthesising qualitative and quantitative evidence: a review of possible methods. J Health Serv Res Policy. 2005, 10: 45-53. 10.1258/1355819052801804.

Boyatzis RE: Transforming Qualitative Information. 1998, Sage: Cleveland

Braun V, Clarke V: Using thematic analysis in psychology. Qual Res Psychol. 2006, 3: 77-101. 10.1191/1478088706qp063oa. [ http://science.uwe.ac.uk/psychology/drvictoriaclarke_files/thematicanalysis%20.pdf ]

Silverman D, Ed: Qualitative Research: Theory, method and practice. 1997, London: Sage

Doyle LH: Synthesis through meta-ethnography: paradoxes, enhancements, and possibilities. Qual Res. 2003, 3: 321-344. 10.1177/1468794103033003.

Barroso J, Gollop C, Sandelowski M, Meynell J, Pearce PF, Collins LJ: The challenges of searching for and retrieving qualitative studies. Western J Nurs Res. 2003, 25: 153-178. 10.1177/0193945902250034.

Walters LA, Wilczynski NL, Haynes RB, Hedges Team: Developing optimal search strategies for retrieving clinically relevant qualitative studies in EMBASE. Qual Health Res. 2006, 16: 162-8. 10.1177/1049732305284027.

Wong SSL, Wilczynski NL, Haynes RB: Developing optimal search strategies for detecting clinically relevant qualitative studies in Medline. Medinfo. 2004, 11: 311-314.

Murphy E, Dingwall R, Greatbatch D, Parker S, Watson P: Qualitative research methods in health technology assessment: a review of the literature. Health Technol Assess. 1998, 2 (16):

Seale C: Quality in qualitative research. Qual Inq. 1999, 5: 465-478.

Spencer L, Ritchie J, Lewis J, Dillon L: Quality in Qualitative Evaluation: A framework for assessing research evidence. 2003, London: Cabinet Office

Boulton M, Fitzpatrick R, Swinburn C: Qualitative research in healthcare II: a structured review and evaluation of studies. J Eval Clin Pract. 1996, 2: 171-179. 10.1111/j.1365-2753.1996.tb00041.x.

Article   CAS   PubMed   Google Scholar  

Cobb A, Hagemaster J: Ten criteria for evaluating qualitative research proposals. J Nurs Educ. 1987, 26: 138-143.

CAS   PubMed   Google Scholar  

Mays N, Pope C: Rigour and qualitative research. BMJ. 1995, 311: 109-12.

Medical Sociology Group: Criteria for the evaluation of qualitative research papers. Med Sociol News. 1996, 22: 68-71.

Alderson P: Listening to Children. 1995, London: Barnardo's

Egger M, Davey-Smith G, Altman D: Systematic Reviews in Health Care: Meta-analysis in context. 2001, London: BMJ Publishing

Sandelowski M, Barroso J: Finding the findings in qualitative studies. J Nurs Scholarsh. 2002, 34: 213-219. 10.1111/j.1547-5069.2002.00213.x.

Sandelowski M: Using qualitative research. Qual Health Res. 2004, 14: 1366-1386. 10.1177/1049732304269672.

Thomas J, Brunton J: EPPI-Reviewer 3.0: Analysis and management of data for research synthesis. EPPI-Centre software. 2006, London: EPPI-Centre, Social Science Research Unit, Institute of Education

Fisher M, Qureshi H, Hardyman W, Homewood J: Using Qualitative Research in Systematic Reviews: Older people's views of hospital discharge. 2006, London: Social Care Institute for Excellence

Dixey R, Sahota P, Atwal S, Turner A: Children talking about healthy eating: data from focus groups with 300 9–11-year-olds. Nutr Bull. 2001, 26: 71-79. 10.1046/j.1467-3010.2001.00078.x.

Daly A, Willis K, Small R, Green J, Welch N, Kealy M, Hughes E: Hierarchy of evidence for assessing qualitative health research. J Clin Epidemiol. 2007, 60: 43-49. 10.1016/j.jclinepi.2006.03.014.

Popay J: Moving beyond floccinaucinihilipilification: enhancing the utility of systematic reviews. J Clin Epidemiol. 2005, 58: 1079-80. 10.1016/j.jclinepi.2005.08.004.

Strike K, Posner G: Types of synthesis and their criteria. Knowledge Structure and Use: Implications for synthesis and interpretation. Edited by: Ward S, Reed L. 1983, Philadelphia: Temple University Press

Marston C, King E: Factors that shape young people's sexual behaviour: a systematic review. The Lancet. 2006, 368: 1581-86. 10.1016/S0140-6736(06)69662-1.

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Acknowledgements

The authors would like to thank Elaine Barnett-Page for her assistance in producing the draft paper, and David Gough, Ann Oakley and Sandy Oliver for their helpful comments. The review used an example in this paper was funded by the Department of Health (England). The methodological development was supported by Department of Health (England) and the ESRC through the Methods for Research Synthesis Node of the National Centre for Research Methods. In addition, Angela Harden held a senior research fellowship funded by the Department of Health (England) December 2003 – November 2007. The views expressed in this paper are those of the authors and are not necessarily those of the funding bodies.

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Thomas, J., Harden, A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol 8 , 45 (2008). https://doi.org/10.1186/1471-2288-8-45

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Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

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Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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Textual analysis is the method of analysing and understanding the text. We need to look carefully at the text to identify the writer’s context and message.

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Chapter 22: Thematic Analysis

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Describe the different approaches to thematic analysis.
• Understand how to conduct the three types of thematic analysis.
• Identify the strengths and limitations of each type of thematic analysis.

What is thematic analysis?

Thematic analysis is a common method used in the analysis of qualitative data to identify, analyse and interpret meaning through a systematic process of generating codes (see Chapter 20) that leads to the development of themes. 1 Thematic analysis requires the active engagement of the researcher with the data, in a process of sorting, categorising and interpretation. 1 Thematic analysis is exploratory analysis whereby codes are not predetermined and are data-derived, usually from primary sources of data (e,g, interviews and focus groups). This is in contrast to themes generated through directed or summative content analysis, which is considered confirmatory hypothesis-driven analysis, with predetermined codes typically generated from a hypothesis (see Chapter 21). 2 There are many forms of thematic analysis. Hence, it is important to treat thematic analysis as one of many methods of analysis, and to justify the approach on the basis of the research question and pragmatic considerations such as resources, time and audience. The three main forms of thematic analysis used in health and social care research, discussed in this chapter, are:

Applied thematic analysis

• Framework analysis
• Reflexive thematic analysis.

This involves multiple, inductive analytic techniques designed to identify and examine themes from textual data in a way that is transparent and credible, drawing from a broad range of theoretical and methodological perspectives. It focuses on presenting the stories of participants as accurately and comprehensively as possible. Applied thematic analysis mixes a bit of everything: grounded theory, positivism, interpretivism and phenomenology. 2

Applied thematic analysis borrows what we feel are the more useful techniques from each theoretical and methodological camp and adapts them to an applied research context. 2(p16)

Applied thematic analysis involves five elements:

• Text s egmentation  involves identifying a meaningful segment of text and the boundaries of the segment. Text segmentation is a useful process as a transcript from a 30-minute interview can be many pages long. Hence, segmenting the text provides a manageable section of the data for interrogation of meaning. For example, text segmentation may be a participant’s response to an interview question, a keyword or concept in context, or a complete discourse between participants. The segment of text is more than a short phrase and can be both small and large sections of text. Text segments can also overlap, and a smaller segment may be embedded within a larger segment. 3
• Creation of the codebook is a critical element of applied thematic analysis. The codebook is created when the segments of text are systematically coded into categories, types and relationships, and the codes are defined by the observed meaning in the text. The codes and their definitions are descriptive in the beginning, and then evolve into explanatory codes as the researcher examines the commonalities, differences and relationships between the codes. The codebook is an iterative document that the researcher builds and refines as they become more immersed and familiar with the data. 3 Table 22.1 outlines the key components of a codebook. 3

Table 22.1. Codebook components and an example

• Structural coding can be useful if a structured interview guide or focus group guide has been used by the researcher and the researcher stays close to the wording of the question and its prompts. The structured question is the structural code in the codebook, and the text segment should include the participant’s response and any dialogue following the question. Of course, this form of coding can be used even if the researcher does not follow a structured guide, which is often the reality of qualitative data collection. The relevant text segments are coded for the specific structure, as appropriate. 3
• Content coding is informed by the research question(s) and the questions informing the analysis. The segmented text is grouped in different ways to explore relationships, hierarchies, descriptions and explanations of events, similarities, differences and consequences. The content of the text segment should be read and re-read to identify patterns and meaning, with the generated codes added to the codebook.
• Themes vary in scope, yet at the core they are phrases or statements that explain the meaning of the text. Researchers need to be aware that themes are considered a higher conceptual level than codes, and therefore should not be comprised of single words or labels. Typically, multiple codes will lead to a theme. Revisiting the research and analysis questions will assist the researcher to identify themes. Through the coding process, the researcher actively searches the data for themes. Examples of how themes may be identified include the repetition of concepts within and across transcripts, the use of metaphors and analogies, key phrases and common phrases used in an unfamiliar way. 3

Framework a nalysis

This method originated in the 1980s in social policy research. Framework analysis is suited to research seeking to answer specific questions about a problem or issue, within a limited time frame and with homogenous data (in topics, concepts and participants); multiple researchers are usually involved in the coding process. 4-6 The process of framework analysis is methodical and suits large data sets, hence is attractive to quantitative researchers and health services researchers. Framework analysis is useful for multidisciplinary teams in which not all members are familiar with qualitative analysis. Framework analysis does not seek to generate theory and is not aligned with any particular epistemological, philosophical or theoretical approach. 5 The output of framework analysis is a matrix with rows (cases), columns (codes) and cells of summarised data that enables researchers to analyse the data case by case and code by code. The case is usually an individual interview, or it can be a defined group or organisation. 5

The process for conducting framework analysis is as follows 5 :

1. Transcription – usually verbatim transcription of the interview.

2. Familiarisation with the interview – reading the transcript and listening to the audio recording (particularly if the researcher doing the analysis did not conduct the interview) can assist in the interpretation of the data. Notes on analytical observations, thoughts and impressions are made in the margins of the transcript during this stage.

3. Coding – completed in a line-by-line method by at least two researchers from different disciplines (or with a patient or public involvement representative), where possible. Coding can be both deductive – (using a theory or specific topics relevant to the project – or inductive, whereby open coding is applied to elements such as behaviours, incidents, values, attitudes, beliefs, emotions and participant reactions. All data is coded.

4. Developing a working analytical framework – codes are collated and organised into categories, to create a structure for summarising or reducing the data.

5. Applying the analytical framework – indexing the remaining transcripts by using the categories and codes of the analytical framework.

6. Charting data into the framework matrix – summarising the data by category and from each transcript into the framework matrix, which is a spreadsheet with numbered cells in which summarised data are entered by codes (columns) and cases (rows). Charting needs to balance the reduction of data to a manageable few lines and retention of the meaning and ‘feel’ of the participant. References to illustrative quotes should be included.

7. Interpreting the data – using the framework matrix and notes taken throughout the analysis process to interpret meaning, in collaboration with team members, including lay and clinical members.

Reflexive thematic analysis

This is the thematic analysis approach developed by Braun and Clarke in 2006 and explained in the highly cited article ‘ Using thematic analysis in psychology ’ . 7 Reflexive thematic analysis recognises the subjectiveness of the analysis process, and that codes and themes are actively generated by the researcher. Hence, themes and codes are influenced by the researcher’s values, skills and experiences. 8 Reflexive thematic analysis ‘exists at the intersection of the researcher, the dataset and the various contexts of interpretation’. 9(line 5-6) In this method, the coding process is less structured and more organic than in applied thematic analysis. Braun and Clarke have been critical of the use of the term ‘emerging themes’, which many researchers use to indicate that the theme was data-driven, as opposed to a deductive approach:

This language suggests that meaning is self evident and somehow ‘within’ the data waiting to be revealed, and that the researcher is a neutral conduit for the revelation of said meaning. In contrast, we conceptualise analysis as a situated and interactive process, reflecting both the data, the positionality of the researcher, and the context of the research itself… it is disingenuous to evoke a process whereby themes simply emerge, instead of being active co-productions on the part of the researcher, the data/participants and context. 10 (p15)

Since 2006, Braun and Clarke have published extensively on reflexive thematic analysis, including a methodological paper comparing reflexive thematic analysis with other approaches to qualitative analysis, 8 and have provided resources on their website to support researchers and students. 9 There are many ways to conduct reflexive thematic analysis, but the six main steps in the method are outlined following. 9 Note that this is not a linear, prescriptive or rule-based process, but rather an approach to guide researchers in systematically and robustly exploring their data.

1.  Familiarisation with data – involves reading and re-reading transcripts so that the researcher is immersed in the data. The researcher makes notes on their initial observations, interpretations and insights for both the individual transcripts and across all the transcripts or data sources.

2.  Coding – the process of applying succinct labels (codes) to the data in a way that captures the meaning and characteristics of the data relevant to the research question. The entire data set is coded in numerous rounds; however, unlike line-by-line coding in grounded theory (Chapter 27), or data segmentation in applied thematic analysis, not all sections of data need to be coded. 8 After a few rounds of coding, the codes are collated and relevant data is extracted.

3.  Generating initial themes – using the collated codes and extracted data, the researcher identifies patterns of meaning (initial or potential themes). The researcher then revisits codes and the data to extract relevant data for the initial themes, to examine the viability of the theme.

4 .  Developing and reviewing themes – checking the initial themes against codes and the entire data set to assess whether it captures the ‘story’ of the data and addresses the research question. During this step, the themes are often reworked by combining, splitting or discarding. For reflexive thematic analysis, a theme is defined as a ‘pattern of shared meaning underpinned by a central concept or idea’. 8 (p 39 )

5.  Refining, defining and naming themes – developing the scope and boundaries of the theme, creating the story of the theme and applying an informative name for the theme.

6.  Writing up – is a key part of the analysis and involves writing the narrative of the themes, embedding the data and providing the contextual basis for the themes in the literature.

Themes versus c odes

As described above, themes are informed by codes, and themes are defined at a conceptually higher level than codes. Themes are broader categorisations that tend to describe or explain the topic or concept. Themes need to extend beyond the code and are typically statements that can stand alone to describe and/or explain the data. Fereday and Muir-Cochrane explain this development from code to theme in Table 22.2. 11

Table 22.2. Corroborating and legitimating coded themes to identify second-order themes

*Note: This table is from an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

When I [the author] first started publishing qualitative research, many of my themes were at the code level. I then got advice that when the themes are the subheadings of the results section of my paper, they should tell the story of the research. The difference in my theme naming can be seen when comparing a paper from my PhD thesis, 12 which explores the challenges of church-based health promotion, with a more recent paper that I published on antimicrobial stewardship 13 (refer to the theme tables in the publications).

Table 22.3. Examples of thematic analysis

Advantages and challenges of thematic analysis

Thematic analysis is flexible and can be used to analyse small and large data sets with homogenous and heterogenous samples. Thematic analysis can be applied to any type of data source, from interviews and focus groups to diary entries and online discussion forums. 1 Applied thematic analysis and framework analysis are accessible approaches for non-qualitative researchers or beginner researchers. However, the flexibility and accessibility of thematic analysis can lead to limitations and challenges when thematic analysis is misapplied or done poorly. Thematic analysis can be more descriptive than interpretive if not properly anchored in a theoretical framework. 1 For framework analysis, the spreadsheet matrix output can lead to quantitative researchers inappropriately quantifying the qualitative data. Therefore, training and support from a qualitative researcher with the appropriate expertise can help to ensure that the interpretation of the data is meaningful. 5

Thematic analysis is a family of analysis techniques that are flexible and inductive and involve the generation of codes and themes. There are three main types of thematic analysis: applied thematic analysis, framework analysis and reflexive thematic analysis. These approaches span from structured coding to organic and unstructured coding for theme development. The choice of approach should be guided by the research question, the research design and the available resources and skills of the researcher and team.

• Clarke V, Braun V. Thematic analysis. J Posit Psychol . 2017;12(3):297-298. doi:10.1080/17439760.2016.1262613
• Guest G, MacQueen KM, Namey EE. Introduction to applied thematic analysis. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
• Guest G, MacQueen, K.M., Namey, E.E.,. Themes and Codes. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
• Srivastava A, Thomson SB. Framework analysis: A qualitative methodology for applied policy research. Journal of Administration and Governance . 2009;72(3). Accessed September 14, 2023. https://ssrn.com/abstract=2760705
• Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol . 2013;13:117. doi:10.1186/1471-2288-13-117
• Smith J, Firth J. Qualitative data analysis: the framework approach. Nurse Res . 2011;18(2):52-62. doi:10.7748/nr2011.01.18.2.52.c8284
• Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol . 2006;3(2):77-101. doi:10.1191/1478088706qp063oa
• Braun V, Clarke V. Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Couns Psychother Res . 2021;21(1):37-47. doi:10.1002/capr.12360
• Braun V, Clarke V. Thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/
• Braun V, Clarke V. Answers to frequently asked questions about thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/faqs/
• Fereday J, Muir-Cochrane E. Demonstrating Rigour Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods . 2006;5(1):80-92. doi: 10.1177/160940690600500107
• Ayton D, Manderson L, Smith BJ. Barriers and challenges affecting the contemporary church’s engagement in health promotion. Health Promot J Austr . 2017;28(1):52-58. doi:10.1071/HE15037
• Ayton D, Watson E, Betts JM, et al. Implementation of an antimicrobial stewardship program in the Australian private hospital system: qualitative study of attitudes to antimicrobial resistance and antimicrobial stewardship. BMC Health Serv Res . 2022;22(1):1554. doi:10.1186/s12913-022-08938-8
• McKenna-Plumley PE, Graham-Wisener L, Berry E, Groarke JM. Connection, constraint, and coping: A qualitative study of experiences of loneliness during the COVID-19 lockdown in the UK. PLoS One . 2021;16(10):e0258344. doi:10.1371/journal.pone.0258344
• Dickinson BL, Gibson K, VanDerKolk K, et al. “It is this very knowledge that makes us doctors”: an applied thematic analysis of how medical students perceive the relevance of biomedical science knowledge to clinical medicine. BMC Med Educ . 2020;20(1):356. doi:10.1186/s12909-020-02251-w
• Bunzli S, O’Brien P, Ayton D, et al. Misconceptions and the acceptance of evidence-based nonsurgical interventions for knee osteoarthritis. A Qualitative Study. Clin Orthop Relat Res . 2019;477(9):1975-1983. doi:10.1097/CORR.0000000000000784

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Thematic analysis in qualitative research.

11 min read Your guide to thematic analysis, a form of qualitative research data analysis used to identify patterns in text, video and audio data.

What is thematic analysis?

Thematic analysis is used to analyse qualitative data – that is, data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

That data might consist of articles, diaries, blog posts, interview transcripts, academic research, web pages, social media and even audio and video files. They are put through data analysis as a group, with researchers seeking to identify patterns running through the corpus as a whole.

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Thematic analysis steps

Image source: https://www.nngroup.com/articles/thematic-analysis/

While there are many types of thematic analysis, the thematic analysis process can be generalised into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

• Familiarisation – During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.
• Coding – Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.
• Identifying theme – Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.
• Reviewing themes – Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organise the themes into a map, or early theoretical framework.
• Defining and naming themes – As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.
• Writing up – At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

Types of thematic analysis

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis emphasises the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process. The codes they assign are specific to them and exist within a unique context that is made up of:

• The data set
• The assumptions made during the setup of the analysis process
• The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

Image source: https://delvetool.com/blog/thematicanalysis

Thematic analysis vs other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

• Thematic analysis vs comparative analysis – Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.
• Thematic analysis vs discourse analysis – Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.
• Thematic analysis vs narrative analysis – Narrative analysis works with stories – it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.
• Thematic analysis vs content analysis – Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Thematic analysis advantages and disadvantages

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Thematic analysis advantages

• Easy to learn – Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge
• Flexible – Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis
• Broadly applicable – Thematic analysis can be used to address a wide range of research questions.

Thematic analysis – the cons

As well as the benefits, there are some disadvantages thematic analysis brings up.

• Broad scope – In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.
• Themes or codes? – It can be difficult for novice researchers to feel confident about the difference between themes and codes
• Language barriers – Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

How can you use thematic analysis for business research?

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening – lots of 3 star reviews indicate there’s some room for improvement for example – but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Analysing themes across video, text, audio and more

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyse text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

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Supporting best practice in reflexive thematic analysis reporting in Palliative Medicine : A review of published research and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG)

Affiliations.

• 1 Waipapa Taumata Rau, The University of Auckland, Auckland, New Zealand.
• 2 University of the West of England, Bristol, UK.
• PMID: 38469804
• PMCID: PMC11157981
• DOI: 10.1177/02692163241234800

Background: Reflexive thematic analysis is widely used in qualitative research published in Palliative Medicine , and in the broader field of health research. However, this approach is often not used well. Common problems in published reflexive thematic analysis in general include assuming thematic analysis is a singular approach, rather than a family of methods, confusing themes and topics, and treating and reporting reflexive thematic analysis as if it is atheoretical.

Purpose: We reviewed 20 papers published in Palliative Medicine between 2014 and 2022 that cited Braun and Clarke, identified using the search term 'thematic analysis' and the default 'relevance' setting on the journal webpage. The aim of the review was to identify common problems and instances of good practice. Problems centred around a lack of methodological coherence, and a lack of reflexive openness, clarity and detail in reporting. We considered contributors to these common problems, including the use of reporting checklists that are not coherent with the values of reflexive thematic analysis. To support qualitative researchers in producing coherent and reflexively open reports of reflexive thematic analysis we have developed the Reflexive Thematic Analysis Reporting Guidelines (the RTARG; in Supplemental Materials) informed by this review, other reviews we have done and our values and experience as qualitative researchers. The RTARG is also intended for use by peer reviewers to encourage methodologically coherent reviewing.

Key learning points: Methodological incoherence and a lack of transparency are common problems in reflexive thematic analysis research published in Palliative Medicine . Coherence can be facilitated by researchers and reviewers striving to be knowing - thoughtful, deliberative, reflexive and theoretically aware - practitioners and appraisers of reflexive thematic analysis and developing an understanding of the diversity within the thematic analysis family of methods.

Keywords: Coding; interpretative; positivism; qualitative; theme; topic summary.

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Conflict of interest statement

Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Hospital personnel perspectives on factors influencing acute care patient outcomes: a qualitative approach to model refinement

• Jessica Ziemek 1 ,
• Natalie Hoge 1 ,
• Kyla F. Woodward 1 ,
• Emily Doerfler 1 ,
• Alison Bradywood 2 ,
• Alix Pletcher 3 ,
• Abraham D. Flaxman 3 &
• Sarah J. Iribarren 1  

BMC Health Services Research volume  24 , Article number:  805 ( 2024 ) Cite this article

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Health systems have long been interested in the best practices for staffing in the acute care setting. Studies on staffing often focus on registered nurses and nurse-to-patient staffing ratios. There were fewer studies on the relationship between interprofessional team members or contextual factors such as hospital and community characteristics and patient outcomes. This qualitative study aimed to refine an explanatory model by soliciting hospital personnel feedback on staffing and patient outcomes.

We conducted a qualitative study using semi-structured interviews and thematic analysis to understand hospital personnel’s perspectives and experiences of factors that affect acute care inpatient outcomes. Interviews were conducted in 2022 with 38 hospital personnel representing 19 hospitals across Washington state in the United States of America.

Findings support a model of characteristics impacting patient outcomes to include the complex and interconnected relationships between community, hospital, patient, and staffing characteristics. Within the model, patient characteristics were positioned into hospital characteristics, and in turn these were positioned within community characteristics to highlight the importance of setting and context when evaluating outcomes. Together, these factors influenced both staff characteristics and patient outcomes, but these two categories also share a direct relationship.

Findings can be applied to hospitals and health systems in a variety of contexts to examine how external factors such as community resource availability impact care delivery. Future research should expand on this work with specific attention to how staffing changes and interprofessional team composition can improve patient outcomes.

Peer Review reports

Introduction

Acute care health systems face ongoing challenges in recruiting and retaining staff to meet the needs of their patients. Best practices in acute care staffing have long been a topic of interest for organizations around the world [ 1 ]. As demonstrated in a recent systematic review covering two decades of research, studies often focus exclusively on the impact of registered nurse (RN) staffing on patient outcomes [ 1 ]. However, patient care is also impacted by staffing levels of other clinical and nonclinical care team members [ 2 ], and outcomes are also influenced by organizational and community factors (termed contextual factors) [ 3 , 4 , 5 ]. For example, in a community with limited skilled nursing facility beds, patients needing this level of care after discharge may experience longer hospital stays [ 6 ], exposing them to risks from adverse events such as inpatient falls or hospital-acquired infections. A better understanding of care team staffing, contextual factors, and their impacts on patient outcomes is vital to ensuring the development and implementation of meaningful policy related to healthcare staffing.

In 2021, the Washington (WA) state legislature passed a bill focused on transparency in healthcare [ 7 ]. The bill directed the state Department of Health to commission an interdisciplinary team to engage hospital personnel throughout the state and examine the relationships between the acute care workforce and patient outcomes by systematically investigating how workforce characteristics such as the number, type, education, training, and experience of staff affects patient mortality and other patient outcomes [ 7 ]. Our team, led by the University of Washington (UW) School of Nursing in collaboration with researchers at the UW Institute for Health Metrics and Evaluation, was selected to conduct this study. To carry out this research, we established partnerships with contributors including hospital leaders, healthcare associations, and union representatives, to ensure that we addressed the multiple contextual elements impacting patient care outcomes as well as examining staffing of the care team more inclusively. The project included four phases: 1) reviewing studies which examined the impact of care team characteristics such as experience and education on patient outcomes; 2) developing a preliminary explanatory model and analysis plan based on the review, contributor input, and available data sources; 3) refining the model using qualitative data from hospital personnel; and 4) completing a quantitative analysis utilizing anonymized state- and hospital-collected healthcare data guided by the refined model.

In phase one, we completed a review of systematic reviews that identified several gaps in existing data [ 8 ]. First, outside of RN staffing ratios or nursing team composition ratios (e.g., RNs and nursing assistants), staffing of the expanded acute care team, including professionals such as therapists or social workers and nonclinical workers such as environmental services, was rarely quantified in health services literature linking staffing to patient outcomes. The second gap was the limited inclusion or assessment of hospital- and community-level factors in studies examining the relationship between staffing and patient outcomes [ 8 ]. The lack of defined factors and the absence of clear frameworks led us to co-develop an explanatory model with key contributors in phase two (Fig.  1 ) [ 8 ]. The purpose of phase three was to assess and refine the model with input from hospital personnel, including identification of missing factors and how all the factors may have contributed to outcomes in various acute care contexts, including impacts on operations, work environment, quality, and outcomes for patients and workers. This paper reports on the process of iterative model refinement by 1) presenting the perspectives of hospital personnel across WA on the preliminary model and factors, and 2) showing how these findings were integrated to refine the model. In the final phase of the project, factors identified in the present study were operationalized for use as independent and control variables in our quantitative analysis of the relationship between staffing and patient outcomes in the state [ 9 ].

Initial model of factors impacting staffing and patient outcomes in acute care hospitals. This model drew on findings from the initial phases of the study including literature review and feedback from key contributors across the state [ 8 ]

Study design

We conducted a qualitative study using semi-structured interviews and thematic analysis to understand hospital personnel perspectives and their experiences of factors that affect acute care patients’ outcomes. To refine our model, qualitative research methods were chosen to explore relationships between factors, including context, mechanisms, and outcomes [ 10 ]. This exploratory approach acknowledges the significance of subjectivity in the data and allows for inductive inquiry [ 11 ]. As a critical component of modeling, an iterative process included updating the model following multiple interviews and then further assessing and revising with subsequent participants. The findings from this study served as a foundation for developing a comprehensive model we called the ‘WA Acute Care hospital Characteristics and patient Outcomes model’ (WACCHO), which considers community, hospital, and patient characteristics that interact with staffing to affect patient outcomes. This study was granted exempt status by the UW and the WA Institutional Review Boards (STUDY00013975).

Participant recruitment

Participants were purposively sampled through announcements targeting acute care hospital administrators and representatives sent to state-wide open subscription hospital email listservs managed by the WA Department of Health. Hospital executives and administrators were also directly emailed to increase participation and ensure participants represented critical access and general acute care hospitals in various regional settings as designated by WA state. Site contacts were asked to invite any hospital representative(s) who could provide perceptions of staffing’s impact on patient outcomes to the interview, so interviews frequently included several participants. Participants were unknown to the research team prior to the interviews.

Data collection

The preliminary explanatory model from study phase 2 was used to develop a semi-structured interview guide which was shared with participants prior to the interview. The model was used to guide exploration of agreement, disagreement, and identification of missing factors from each category of the model. Model categories included hospital characteristics and external factors, patient characteristics, staffing characteristics, and patient outcomes. Open-ended questions explored factors, mechanisms, contextual elements, and additional factors that could potentially influence patient outcomes. Interviewers also presented facility-specific data, asked participants for their perceptions of accuracy, and discussed the basic analysis plan for the quantitative portion of the study. A list of interview questions and prompts is provided in Additional File 1. Interviews were conducted between January and June 2022 via video conferencing by two to five members of the research team. Each participant was interviewed once, either individually or with other participants from the same organization, and all interviews were audio recorded and transcribed for analysis. The research team introduced themselves and explained the purpose of the study. Upon obtaining oral consent from participants, the team conducted the interview, and two members of the research team (SI, NH) took detailed field notes. Transcripts were uploaded to ATLAS.ti (version 9).

Both deductive and inductive methods were used in the thematic analysis of data [ 11 ]. An initial codebook was created based on our initial model and interview notes, and emergent codes were added inductively during analysis [ 5 ]. Five team members (NH, JZ, ED, KN, KB) contributed to coding. They met weekly to review codes, ensure a uniform interpretation and application of the coding framework, and address any discrepancies. At least two researchers coded a portion of each transcript to ensure consistency. Once coding was completed, codes were iteratively organized into main themes and subthemes to capture the range of narratives [ 5 ]. Data saturation was determined when no new themes were identified in final interviews [ 12 ]. We followed the consolidated criteria for reporting qualitative research guidelines (COREQ) to ensure comprehensive reporting [ 13 ].

Participants

A total of 20 interviews were conducted with 38 participants from 19 hospitals in eight out of nine regions across WA. Participants worked at three main types of hospitals: acute care (23/38, 60%), critical access (11/38, 29%), and sole community hospitals (4/38, 11%). While the definitions of hospital types may vary in some literature, the Centers for Medicare and Medicaid Services (CMS) officially designates critical access and sole community hospitals as specific types of acute care hospitals which are typically smaller and located in rural settings [ 14 , 15 ]. Participants included a broad range of executives and administrators (23/38, 61%), directors and managers (10/38, 26%), and care team members (5/38, 13%) from all three hospital types. Mean interview length was 61 min.

Explanatory model

The final explanatory model represents the primary common factors and drivers impacting patient outcomes in acute care hospitals based on hospital personnel perspectives (Fig.  2 ). Changes to the initial model (Fig.  1 ) [ 8 ] included the division of the external factors category into community characteristics and hospital characteristics , positioning of patient characteristics into hospital characteristics and hospital characteristics into community characteristics to highlight the interrelatedness between the categories as identified by hospital personnel. The new community characteristics category impacts both staffing characteristics and patient outcomes, while staffing and patient outcomes continue to be directly connected. The following section presents findings organized by model category, and Table  1 provides the number of hospitals out of 19 reporting on each of the main themes within the four model categories.

Refined explanatory model of factors impacting staffing and patient outcomes in acute care hospitals. Study findings were used to refine and enhance the initial model, developing an explanatory model for use in subsequent phases of the project

Community characteristics

Community characteristics were defined as factors outside of the hospital’s control which impacted staffing or patient care, including sociopolitical, geographical, and economic factors, and availability of healthcare resources.

Location and community resources

Participants often described the difficulty of discharging patients to the appropriate level of care due to resource and facility availability, including higher acuity transfers to a referral hospital, or discharge to subacute care such as a skilled nursing facility. When resources for the appropriate level of care were limited, the hospital must keep patients in acute care beds, limiting available resources for other patients. These challenges were more pronounced in rural settings with fewer community resources.

Community characteristics also impacted staffing. Both rural and urban participants described how location and community resources like affordable housing, public transportation, and commute times made it difficult to recruit and retain hospital staff. A participant from a metropolitan area hospital noted that, “Our entry level and mid-level workers cannot afford to work at [hospital name]. They are driving 35, 45 min, an hour, each way just to come to work here.” Similarly, a chief nursing officer from a hospital in a rural setting also noted housing and commute as community factors impacting staffing, “Even if I hire somebody and if they're willing to move here, they can't get housing… one of the OR [operating room] nurses I'm losing, is because the commute is too long. It’s about an hour and half for her.” Additionally, participants in rural locations noted the difficulty in recruiting staff when they were not close to or connected with teaching institutions producing new graduates.

Participants discussed their facilities’ unique challenges due to both the populations they served and health disparities present within the community. One acute care hospital administrator listed some of the challenges faced by the populations served by their hospital such as, “access to core necessities. So, transportation, food, medication, housing...” Another community level challenge was changing seasonal populations. One hospital administrator at a critical access hospital noted the following: “ One of the things... about out here, is the 300 days of sunshine. We get all kinds of visitors. You know our town is 5000 people, but in the summertime, it would be 30,000 people. They come in for fishing, and the weather, and rodeos and other things. So, I know other critical access hospitals that are in rural areas with water and different things that have faced the same kind of thing. You never know how many people that are going to come in.” Multiple population characteristics were identified by participants as leading to unique needs in the acute care setting, including homelessness, homes without basic utilities (e.g., running water or electricity), health insurance in the community, and transient seasonal populations including migrant workers and summer tourists.

Hospital characteristics

Hospital characteristics were defined as the structural and functional qualities of acute care hospitals that influenced the services they offered and the complexity of patients they served.

Hospital type and access to resources

Participants stated that their hospital type, specifically size and connection to larger health systems, influenced their access to resources. Critical access hospitals described more limited access to the relationships and knowledge larger health systems share, including inefficiencies in changing practices. As one administrator said, “… being part of a system really changes things as well, because if it’s a system, then the system itself collaborates and has greater resources to roll forward processes that have been vetted at a higher level.” Various participants described limited budgets and reduced access to equipment secondary to supply chain constraints. Critical access hospitals identified their smaller size and limited resource pool as reasons they must be more particular with capital investments that would enable them to care for more complex patients while simultaneously having the obligation to provide specialty services that were not otherwise available in their communities.

Hospital leadership structure and culture as foundational to quality

Participants considered staffing and leadership culture as a product of organizational priorities that influenced staff satisfaction and quality outcomes. They identified such as access to adequate equipment and supplies as important to providing quality patient care. As one acute care hospital administrator described, “ Something as simple as an overbed table… When we talked about this at incident command…the answer was no. And then, thank God, our CEO is also a nurse and she’s like no, this is basic to taking care of patients and keeping them from falling.” Participants also listed other structural and cultural characteristics including union status, staffing strategies, and budgets as impacting patient care and patient care outcomes.

Participants noted organizational features that emphasized safety culture, with elements like care quality and improved organizational processes. Multiple participants referenced standardized protocols as a safety tool that contributed to improved patient care. Participants also felt an organizational focus on safety and transparency improved staff satisfaction and quality of care. Comments referenced the importance of continuous quality improvement and a focus on process improvement instead of individual errors.

Influence of organizational culture on work environment and staff retention

Participants agreed that the culture of an organization influenced both the work environment and staff retention. They described approaches to support and engage with staff which promoted a positive organizational culture. One approach included providing staff with incentives and benefits such as increased pay, bonuses, parking passes, flexible shifts, and scheduling. Other examples included programs which covered the cost of nursing education in exchange for commitment to work in a given facility for a period of time. Consequently, insufficient organizational culture can lead to staff turnover, as noted by one care team member , “if you’re not given the tools to do your job well, anybody with any empathy is going to go find something else to do.” Participants also presented upstream approaches which improved the work environment, such as involving workers in organizational decision making and appropriate staffing of the interprofessional team.

Units vary across and within hospitals

When discussing data metrics, participants often discussed the difficulty in making comparisons of the same unit between different hospitals and comparisons of units within the same hospital. They expressed confusion with how acuity is defined, especially when comparing patient care across different facilities. Participants felt it was too difficult to use case mix index, a metric used to identify the diversity and severity of patients cared for at specific hospitals, to compare outcomes between units within a hospital or across healthcare systems. Participants did not think case mix encompasses all the variables that should be considered when evaluating the complexities of the patient and the care infrastructure.

Patient characteristics

Patient characteristics were defined as individual demographic, social, and health characteristics of patients admitted to the hospital that may impact the level of care needed.

Underlying health conditions impact the intensity of care

Participants used the term ‘care intensity’ to describe how patient care needs impacted work demands on staff, with agreement that the care intensity is not always directly tied to the patient’s admitting diagnoses or assigned acuity. Participants reported this disparity between acuity and care intensity as a challenge to accurately predict staffing needs. They noted that specific health conditions with higher care intensity included aggressive behavior, traumatic brain injury, obesity, substance use, and dementia. When discussing resource intensive patients, one participant described that, “it generally is a lot of, uh, psychosocial intervention for these people... it’s usually not necessarily relatable to what the acuity of their medical diagnosis is. In fact, it’s frequently not. So it almost needs to be on its own scale, acuity scale... to really accurately reflect the amount of staff time it takes.”

Participants described different strategies to account for care intensity variations, such as having a centralized staffing office or a predefined team who coordinated activities to accommodate rapid and fluctuating changes in staffing needs. In addition to increased care intensity and inpatient staffing demands, patients with certain underlying conditions were difficult to discharge due to the availability of appropriate care in the community or mandated social supports such as individuals needing guardian assignment.

Social history and economic characteristics impact health status

In addition to overall population characteristics, individual demographics, social determinants of health (SDOH), and insurance status of patients influenced their care needs. Factors such as access to routine care, prior healthcare utilization, and comorbidities impacted care intensity and resources needed for patient care.

Staffing characteristics

Staffing characteristics were defined as acute care team members, their roles, and aspects of staffing which influence how facilities provide staff and deliver patient care.

Care team composition and the central role of nurses

When considering the relationship between staffing and patient outcomes, participants discussed team members who contribute to the care team and work in tandem to provide patient care. Participants mentioned roles in multiple professions including physicians, advanced practice providers, RNs, certified nursing assistants, occupational therapists, PTs, pharmacists, social workers, dietary aides, environmental service workers, billing/coding staff, students, and others. Care team members were generally categorized as either clinical, non-clinical, or temporary roles. There was a lack of agreement around the types and breadth of roles included in the acute care team. However, participants discussed state mandated annual RN staffing plans and nurse-to-patient ratios, highlighting the significant role and value placed on RNs in acute patient care and care teams.

Influence of staffing type on work environment

Participants emphasized the importance of differentiating between temporary (e.g., contract, agency, or travel) and permanent RNs when examining how staffing impacted patient outcomes. Participants expressed that temporary workers may be less familiar with facility policies and may not have the same unit-specific training as permanent staff. Additionally, facilities with a larger proportion of rotating temporary workers may not have an established culture of communication and support, which diminishes the quality of the work environment and negatively influences patient outcomes.

RN absorption of non-nursing duties resulting in the dilution of nursing care roles

Participants presented instances when facilities had difficulty filling staffing roles, so RNs absorbed responsibilities, diluting the scope of nursing practice. For example, one sole community hospital administrator stated, “If you’re short PT assistants or PT aids, that falls back on the RN and the nursing assistant. If you don’t have case management or social work, that also falls on the RN. Everything falls on the RN, if... the rest of the team is missing.” Although facilities submit annual nurse staffing matrices, participants frequently spoke to the need to deviate from planned models, highlighting variation in direct and indirect patient support staff which make nurse-to-patient ratios in one setting incomparable to the same workload in another setting.

Education, training, and experience

Discussions around education, training, and experience centralized around nursing staff and focused on the nuances of the term ‘experience.’ Participants agreed that RN experience was complex and difficult to capture, quantify and standardize. Various metrics for measuring experience were presented and considered, such as years of RN or inpatient experience and unit tenure. Participants also quantified RN experience with standards such as a novice to expert or years since licensure. Degrees, licenses, and certifications were discussed as components of education, with several participants stating that RN training was not well documented except in human resource records. Participants noted that overall training and experience on the unit influenced their ability to staff appropriately for patient acuity and diagnosis. When units had higher numbers of staff with more training and experience, the unit could manage more complex patients, yet in many locations, the limited number of experienced staff made patient assignments difficult.

Patient outcomes

Patient outcomes included metrics pertaining to characteristics of a patient’s stay at a hospital and the time immediately following discharge, which were a collection of quality and safety metrics tracked by the hospital and the state.

Impact of staffing on patient outcomes

When asked about patient outcomes, participants described some measures as more sensitive to staffing than others. Participants specifically mentioned falls and pressure ulcers as staffing-sensitive outcomes, with one hospital administrator noting that, “one of the things…making a significant impact on patient outcomes or patient satisfaction and staffing is the number of non-hospital nurses that we have here. So, we have 72 travelers, and we have FEMA [Federal Emergency Management Agency] staff, and so our fall rates increased, our HAPIs [hospital-acquired pressure injuries] have increased, complaints have increased.” Participants characterized staffing-susceptible outcomes as being dependent on care team composition and staffing type rather than the specific number of staff or staff-to-patient ratios.

Cumulative impacts on patient outcomes

Several participants described the influence of community and patient characteristics on patient outcome metrics. An example of this is length-of-stay, defined as the number of days a patient is cared for in an acute care facility. One critical access hospital administrator stated, “it happens, where we cannot get a patient out. We don’t have a receiving hospital or we don’t have EMS [Emergency Medical Services]... that’s the challenge of being... rural.” Length of stay and other outcomes like readmission rates were also significantly impacted by factors outside of staffing control, for example when patients need social support or skilled nursing care that is not readily available in the community at the time of discharge.

This study produced critical findings on factors influencing staffing and patient outcomes in the acute care setting. Some findings reinforce existing knowledge–such as the importance of adequate RN staffing–and others confirm gaps in both knowledge and theory related to care team staffing more broadly and strategies to account for different resource availability in diverse settings. The discussion highlights the gaps in each of the categories in our model with the knowledge that factors are often interconnected and responsive to dynamic changes in other model components. For example, changes in hospital leadership may impact both hospital and staffing characteristics in ways that subsequently change patient outcomes, and changes in community infrastructure or policy can impact access to health resources.

Participants practiced in a wide array of settings and consistently brought forward the need to account for different contexts when considering healthcare staffing policy. Findings suggest that a ‘one size fits all’ approach to staffing is undesirable, instead emphasizing the need for individual organizations to account for their communities and settings when establishing staffing standards and setting outcome targets [ 16 ]. This viewpoint is consistent with implementation science theories such as the Consolidated Framework for Implementation Research [ 17 ], which emphasizes the inclusion of contextual factors when planning, developing, implementing, and evaluating a practice or policy change. Accounting for community contexts allows organizations to attend to the populations they serve and the resources available in their settings. For example, communities with lower demand for inpatient beds and more limited access to skilled nursing facilities may need the flexibility to provide a lower level of care (e.g., a higher patient to nurse ratio) when a patient ready for skilled nursing is still physically present in the hospital [ 6 ].

While organizational culture has been linked with workforce outcomes such as RN turnover and retention [ 18 ], participants indicated that elements of culture were also vital to conversations about staffing and patient outcomes. An organizational emphasis on safety and just culture provides opportunities for workers to provide input on staffing needs and challenges. Within just culture, transparency and psychological safety work bidirectionally to ensure that staff can bring forward concerns without penalty and that management and administration share information on their own challenges and progress related to staffing [ 19 ].

Several proven strategies for approaching this type of culture are Magnet® designation, which emphasizes the involvement of RNs in hospital administration, policy, and practice [ 20 ], and American Association of Critical Care Nurses’ Healthy Work Environment, which identifies 6 critical elements to a just unit culture [ 21 ]. Accounting for features of organizational culture using an established framework such as these would help provide additional information and clarity into organizational practices around staffing, which may be an important predictor or mediator of the relationship between staffing and patient outcomes.

Hospital environment and culture impact patient care in other ways. For example, one participant’s recollection of a discussion about bedside tables shows how a leader with bedside experience recognized the importance of a piece of equipment in promoting patient safety. In addition to these administrative types of decisions, structural and logistical features of hospitals impact staffing and workload. For example, when patient care supplies were not readily available, nurses or other direct care staff had to leave the unit to retrieve them, taking time and focus away from patient care.

Patients with different types of acute care issues had various levels of need, often represented in terms of patient acuity or some measure of nursing hours invested in care [ 8 ]. In our study, despite consensus across participants that the unique care needs of individual patients were not routinely captured in acuity measures or admitting diagnoses, there was no agreement on a standardized way these needs could be measured or reported. High care intensity, stemming from the intersection of behavioral, mental, and physical health status, required additional work from the care team. Participants indicated that these situations disrupt the unit's workflow and change staffing needs, even when no additional staff were available. While care quality initiatives aim to increase inpatient assessment of SDOH, these assessments may indicate a need for more resources than staff have available to address issues. Overall, a more nuanced understanding of patient care intensity as it affects the unit workload is necessary when evaluating staffing practice and policies.

Staffing has been a topic of interest in health services literature for decades, with most data focused on RN staffing levels [ 1 ]. One of the main issues identified in our scoping review and reiterated by participants in this study was the lack of consistency around defining a ‘care team’, with terminology like interprofessional or multidisciplinary  teams excluding nonclinical team members and the relative absence of any non-nursing roles in staffing plans or evaluation [ 8 ]. Existing data show the essential nature of interprofessional teams in optimizing patient outcomes [ 22 , 23 ], but focus almost exclusively on teams with clinical roles rather than supportive roles and services. In this study, participants brought forward concerns about what work the RN is doing when other staff were missing and how doing that work impacted their availability to perform needed nursing tasks. Diluting RN time with non-nursing tasks means that RNs were not working at the top of their scope of practice, which leads to dissatisfaction and connects to burnout and turnover [ 24 ]. Similarly, when there were not enough RNs with the training and experience to care for certain types of patients, patient outcomes may suffer [ 25 ]. In order to develop meaningful policy related to staffing, a more inclusive and holistic definition of the care team is required.

When assessing patient outcomes in health services research, data are often sourced from statewide administrative bodies and include a range of quality metrics such as falls, skin breakdown, length of stay, mortality, and patient satisfaction. While measures like falls and skin breakdown are often labeled as “nursing sensitive”, participants indicated that nurses were not the only staff members whose presence or tasks may impact those outcomes. For example, if typical resources such as PT or PT aides were unavailable to ambulate a patient, the RN may not be able to add that task to their workload, leading to skin breakdown. In this case, the ‘nurse-sensitive’ indicator may not tell the whole story about staffing.

Other patient outcomes like length of stay or readmission may be more indicative of community resources. For example, the availability or staffing levels of residential facilities that care for patients with sequelae of brain injury may mean that patients linger in the acute care setting or get sent back to the emergency room if facility staff were unable to handle symptoms. These types of influences are rarely accounted for in studies which focus on direct measures of nursing staffing and patient outcomes in acute care.

Patient outcomes also vary when underlying conditions or characteristics, including SDOH, impact overall health and complexity of services needed in the acute care setting [ 26 ]. WA state now requires hospitals to report certain data on SDOH to the Department of Health [ 7 ], which will improve the ability to account for these characteristics in future analyses of patient outcomes and provide more conclusive evidence related to health equity in different patients and communities.

Implications

Altogether, our findings provide a framework for examining relationships between staffing and patient outcomes more robustly, including components which are currently missing in most health services and health workforce research. Our refined model can be used to guide examination of the ‘new normal’ experienced in healthcare settings following the pandemic, where staffing of multiple care team roles has been unstable and community and organizational characteristics may undergo substantial change.

Findings also reinforce the difficulty of applying a blanket nurse staffing policy to individual organizations. To ensure safe staffing levels at the local, state, or national level, policy needs to reflect more than just the numbers of a specific type of staff at the bedside, instead drawing on a more comprehensive understanding of the communities, settings, and patients served at different facilities. This process may require more robust data collection and policy and budget commitment to ensuring an adequate supply of healthcare workers to achieve high quality outcomes.

Limitations

This study had several notable limitations. First, the timing of interviews during the COVID-19 pandemic made it challenging for hospital representatives to participate. Frontline staff were often unavailable, and leaders were frequently supporting patient care activities during surges in admissions. This resulted in less robust representation from some care team members and more prominent representation of executive and administrator perspectives, which may have focused the discussion on nurse staffing structures rather than perceptions of patient needs. Second, as the study focused on experiences before the pandemic, participants were asked to remember past perceptions, which challenged their focus and could have led to limited recall bias. Finally, as our model was iteratively developed throughout the interview period, interview questions were not static and discussion may have focused on elements that participants felt more strongly about, influencing the quantity of participant feedback on specific elements of the model.

Altogether, this study enhanced the initial findings of our scoping review by providing insight from healthcare personnel in several types of acute care hospitals across the state. Findings highlight the complexity and interrelatedness of the categories in the model, while drawing attention to critical gaps that must be addressed to better understand how communities, organizations, patients, and staffing all impact patient outcomes. Our study highlights the need to ensure that RN-centered care teams include appropriate care team staffing to meet the needs of patients, and that access to community resources is critical both for ensuring that patients receive efficient continuity of care throughout their recovery and seeing that acute care beds and staff are appropriately used. Future research should expand on this study to better understand lessons learned from the COVID-19 pandemic and the ‘new normal’ state of healthcare, with specific attention to staffing changes and care team composition that can direct future work to improve patient outcomes. Ensuring optimal staffing of care teams also has the potential to decrease burnout, leading to improved outcomes for acute care staff and improved retention of this vital workforce.

Availability of data and materials

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

Abbreviations

Registered nurse

Washington Acute Care hospital Characteristics and patient Outcomes model

Centers for Medicare and Medicaid Services

Chief executive officer

Physical therapist

Federal Emergency Management Agency

Hospital-acquired pressure injury

Social determinants of health

Dall’Ora C, Saville C, Rubbo B, et al. Nurse staffing levels and patient outcomes: a systematic review of longitudinal studies. Int J Nurs Stud. 2022;104311; https://doi.org/10.1016/j.ijnurstu.2022.104311 .

Rubbo B, Saville C, Dall’Ora C, et al. Staffing levels and hospital mortality in England: a national panel study using routinely collected data. BMJ Open. 2023;13(5): e066702. https://doi.org/10.1136/bmjopen-2022-066702 .

Article   PubMed   PubMed Central   Google Scholar  

Alexander GR, Johnson JHJ. Disruptive demographics: their effects on nursing demand, supply and academic preparation. Nurs Admin Quart. 2021;45(1):58–64. https://doi.org/10.1097/naq.0000000000000449 .

Article   Google Scholar  

Friese CR, Xia R, Ghaferi A, Birkmeyer JD, Banerjee M. Hospitals in ‘magnet’ program show better patient outcomes on mortality measures compared to non-‘magnet’ hospitals. Health Aff. 2015;34(6):986–92. https://doi.org/10.1377/hlthaff.2014.0793 .

Al-Amin M. Hospital characteristics and 30-day all-cause readmission rates. J Hosp Med. 2016;11(10):682–7. https://doi.org/10.1002/jhm.2606 .

Article   PubMed   Google Scholar  

Zhao EJ, Yeluru A, Manjunath L, et al. A long wait: barriers to discharge for long length of stay patients. Postgrad Med J. 2018;94(1116):546–50. https://doi.org/10.1136/postgradmedj-2018-135815 .

Washington House of Representatives. Hospital transparency and reporting. HB 1272. Olympia; 2021. https://lawfilesext.leg.wa.gov/biennium/2021-22/Pdf/Bill%20Reports/House/1272-S2.E%20HBR%20FBR%2021.pdf?q=20230516090443 .

Pletcher A, Woodward K, Hoge N, et al. Interprofessional care team, staffing, and setting characteristics that impact patient outcomes: a review of reviews. medRxiv 2024 [preprint]; https://doi.org/10.1101/2024.01.04.24300868 .

Blair-Stahn ND, Woodward KF, Iribarren SJ, Pletcher A, Hoge N, Flaxman A. The relationship between staffing and adverse events in Washington State hospitals: a cross-sectional study using linked hospital data. Research Square 2024 [preprint]; https://doi.org/10.21203/rs.3.rs-3979968/v1

Bonell C, Warren E, Melendez-Torres G. Methodological reflections on using qualitative research to explore the causal mechanisms of complex health interventions. Evaluation. 2022;28(2):166–81. https://doi.org/10.1177/13563890221086309 .

Maguire M, Delahunt B. Doing a thematic analysis: a practical, step-by-step guide for learning and teaching scholars. Irel J High Educ. 2017;9(3):1–14. https://ojs.aishe.org/index.php/aishe-j/article/view/335 .

Saunders B, Sim J, Kingstone T, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52(4):1893–907. https://doi.org/10.1007/s11135-017-0574-8 .

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57. https://doi.org/10.1093/intqhc/mzm042 .

Center for Medicare & Medicaid Services. Critical Access Hospitals . 2021. Available from: https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/cahs .

Health Resources and Services Administration. Sole Community Hospitals. 2022. Available from: https://www.hrsa.gov/opa/eligibility-and-registration/hospitals/sole-community-hospitals .

Diaz A, O’Reggio R, Norman M, Thumma JR, Dimick JB, Ibrahim AM. Association of historic housing policy, modern day neighborhood deprivation and outcomes after inpatient hospitalization. Annals Surg. 2021;274(6):985–91. https://doi.org/10.1097/sla.0000000000005195 .

Damschroder LJ, Reardon CM, Widerquist MAO, et al. The updated consolidated framework for implementation research based on user feedback. Implement Sci. 2022;17(1):75. https://doi.org/10.1186/s13012-022-01245-0 .

Woodward K, Willgerodt M. A systematic review of RN turnover and retention in the United States. Nurs Outlook 2022;70(4); https://doi.org/10.1016/j.outlook.2022.04.005 .

Dekker SW, Breakey H. ‘Just culture:’Improving safety by achieving substantive, procedural and restorative justice. Saf Sci. 2016;85:187–93. https://doi.org/10.1016/j.ssci.2016.01.018 .

American Nurses Credentialing Center. ANCC Magnet recognition program ® . n.d. Available from: https://www.nursingworld.org/organizational-programs/magnet/about-magnet/ .

American Association of Critical-Care Nurses. AACN healthy work environment. https://www.aacn.org/nursing-excellence/healthy-work-environments . Published 2020. Accessed November 5, 2020.

Epstein NE. Multidisciplinary in-hospital teams improve patient outcomes: a review. Surg Neurol Int. 2014;5(Suppl 7):S295–303. https://doi.org/10.4103/2152-7806.139612 .

Donovan AL, Aldrich JM, Gross AK, et al. Interprofessional care and teamwork in the ICU. Crit Care Med. 2018;46(6):980–90. https://doi.org/10.1097/ccm.0000000000003067 .

Gottlieb LN, Gottlieb B, Bitzas V. Creating empowering conditions for nurses with workplace autonomy and agency: how healthcare leaders could be guided by strengths-based nursing and healthcare leadership (SBNH-L). J Healthc Leadersh. 2021;13:169. https://doi.org/10.2147/JHL.S221141 .

Evangelou E, Lambrinou E, Kouta C, et al. Identifying validated nursing quality indicators for the intensive care unit: an integrative review. Connect World Crit Care Nurs. 2018;12(2):28–39. https://doi.org/10.1891/1748-6254.12.2.28 .

Wammes JJG, van der Wees PJ, Tanke MAC, Westert GP, Jeurissen PPT. Systematic review of high-cost patients’ characteristics and healthcare utilisation. BMJ Open. 2018;8(9): e023113. https://doi.org/10.1136/bmjopen-2018-023113 .

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Acknowledgements

We thank the following individuals for their time and contributions to the study: Katie Ann Blanchard (KB), MN, RN, PhD student in Nursing Science, and Kat Ng, BSN, RN, CNOR, Master of Science student in Clinical Informatics and Patient-Centered Technologies who participated in initial qualitative coding. Nathaniel Blair-Stahn participated in interviews, presented hospital-level data and incorporated feedback into the final study analysis. We are grateful to our colleagues who supported the study and helped connect us with key contributors: Gloria Brigham at the Washington State Nursing Association, Darcy Jaffe at the Washington State Hospital Association, and Jim Janson at Washington Department of Health.

This study was funded by the Washington State Department of Health (DOH Contract study HED26380 with the University of Washington’s School of Nursing). Dr. Woodward’s work was supported by the National Institutes of Health, National Institute of Nursing Research Training Program in Global Health Nursing at the University of Washington (T32 NR019761). Ms. Ziemek's work was supported by the National Institutes of Health, National Institute of Nursing Research Training Program in Biology to Society at the University of Washington (T32016913).

The content is solely the responsibility of the authors and does not necessarily represent the official views of Washington State or the National Institutes of Health.

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Contributions

AF and SI conceptualized and designed the project. NH, AP, AF, and SI participated in data acquisition. JZ, NH, ED, AB, and SI provided analysis and interpretation. JZ, KW, ED, and SI were major contributors in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kyla F. Woodward .

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Additional file 1. Semi-structured interview guide. Guide includes a list of the questions and prompts used in stakeholder interviews.

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Ziemek, J., Hoge, N., Woodward, K.F. et al. Hospital personnel perspectives on factors influencing acute care patient outcomes: a qualitative approach to model refinement. BMC Health Serv Res 24 , 805 (2024). https://doi.org/10.1186/s12913-024-11271-x

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ORIGINAL RESEARCH article

Islands in the stream: a qualitative study on the accessibility of mental health care for persons with substance use disorders in belgium.

• 1 Department of Special Needs Education, Ghent University, Ghent, Belgium
• 2 EQUALITY//ResearchCollective, HOGENT University of Applied Sciences, Ghent, Belgium
• 3 Institut de recherche santé et société, Université Catholique de Louvain, Woluwe-Saint-Lambert, Belgium
• 4 Vakgroep Psychiatrie en Neuropsychologie, Universiteit van Maastricht, Maastricht, Netherlands

Introduction: Persons with substance use disorders (SUD) make up a considerable proportion of mental health care service users worldwide. Since 2010, Belgian mental health care has undergone a nationwide reform (‘Title 107’) aiming to realize a mental health care system that fosters more intensive collaboration, strengthens the cohesion and integration across and between different services, and is more responsive to the support needs of all service users. Although persons with SUD were named as a prioritized target group, how this reform impacted the lives and recovery journeys of persons with SUD remains understudied. This study aims to investigate how persons with SUD, regardless of whether they have co-occurring mental health issues, experience the accessibility of mental health care in light of the ‘Title 107’ reform.

Methods: Data were collected by means of in-depth interviews with a heterogeneous sample of persons with SUD (n=52), recruited from five regional mental health networks in Belgium. In-depth interviews focused on experiences regarding (history of) substance use, accessibility of services and support needs, and were analyzed thematically.

Results: Five dynamic themes came to the fore: fragmentation of care and support, the importance of “really listening”, balancing between treatment-driven and person-centered support, the ambivalent role of peers, and the impact of stigma.

Discussion: Despite the ‘Title 107’ reform, persons with SUD still experience mental health care services as ‘islands in the stream’, pointing to several pressing priorities for future policy and practice development: breaking the vicious cycles of waiting times, organizing relational case management, tackling stigma and centralizing lived experiences, and fostering recovery-promoting collaboration.

1 Introduction

It is widely acknowledged that persons with substance use disorders (SUD) account for a considerable proportion of the targeted service user population in mental health care worldwide ( 1 , 2 ). Although prevalence figures vary, it is estimated that up to 50 percent of persons with mental health problems have concurrent SUD and vice versa ( 3 – 5 ). Today, there is consensus that recovery from SUD is a highly idiosyncratic and complex process impacting multiple life domains, in which health, personal growth, quality of life, inclusive citizenship and social participation are important dimensions of change ( 6 ). While there are several pathways to initiate and maintain recovery (both treatment-assisted and unassisted), a range of integrative and multidimensional treatment modalities are generally put forward as the best-suited to meet the heterogeneous needs and support the recovery journeys of persons with SUD ( 7 , 8 ). Still, however, the treatment coverage of persons with SUD remains poor. A global study by Degenhardt and colleagues showed that only 7.1% of persons with past-year SUD received adequate support in high-income countries, 4.3% in upper-middle-income countries and 1.0% in low-income countries ( 9 ).

One key condition for realizing more recovery-oriented, integrative and person-centered support for persons with SUD is intensive collaboration and exchange of expertise between generic mental health care services and specialized addiction treatment services ( 10 , 11 ). In Belgium, mental health care and specialized addiction treatment have traditionally functioned as two categorically separate sectors. However, from the 1990s onwards, consensus grew to reorganize the mental health field in favor of more integrative care that is more competent and sensitive towards the support needs of specific groups, such as persons with SUD ( 12 ). Since 2010, Belgian adult mental health care underwent a nationwide transformation, referred to as the ‘Title 107’ reform, aiming at promoting community-based support, strengthening continuity and integration of care and reducing the long-term uptake of psychiatric hospital beds ( 13 ). Through this reform, the Belgian mental health care landscape was divided into 20 regional mental health networks, responsible for providing five care functions: (1) primary mental health care, (2) outreach, (3) social integration and recovery, (4) intensive inpatient care, and (5) long-term residential facilities. These functions are operationalized through pre-existing and newer initiatives, including mobile teams providing mental health care at home, psychosocial rehabilitation centers, intensified short-term residential treatment and supported housing initiatives. One of the core incentives of this reform was de-categorization, i.e. the implementation of collaborative procedures and the strengthening of cohesion between and across different services to supply integrated care across different welfare sectors ( 14 ). In the original ‘Title 107’ blueprint, persons with SUD were explicitly named as one of the priority target groups for the mental health care networks. To date, however, no specific action has been undertaken to deliver care tailored to their support needs. Moreover, in 2019, the Belgian Healthcare Knowledge Center raised that there remained several organizational barriers to appropriate mental health care for persons with complex needs, including persons with SUD and co-occurring mental health problems ( 15 ). Likewise, international research illustrates that issues relating to poor collaboration within and across the mental health care and specialized addiction treatment sectors persist and reinforce barriers to adequate care (e.g. waiting lists, lack of staff training, stigma) ( 13 , 16 ).

These challenges are also reflected in a recent WHO report cautioning that the human rights of persons with mental health problems (as stated in the UN Convention of Rights of Persons with Disabilities) remain violated (e.g. in terms of accessibility, discrimination, full participation) despite de-institutionalization reforms globally ( 17 , 18 ). The growing pains and persistent challenges of nationwide mental health reform significantly and directly affect the micro-level everyday lives, recovery experiences and care trajectories of service users. However, this impact remains understudied, as research focusing on macro-level (i.e. networks) and meso-level (i.e. services and professional stakeholders) evaluations and developments of such reforms have thus far been prioritized (e.g., 19 – 21 ).

At the heart of any macro-level mental health reform lies the ambition to positively affect the lives and recovery journeys of individual service users. However, these high-level reforms unintentionally risk reproducing existing barriers and creating new challenges to delivering adequate support ( 22 ). To cultivate suitable and efficient strategies for addressing these barriers, it is imperative to gain insight into how they manifest in the everyday lives of service users ( 23 ). In the Belgian case, although persons with SUD were explicitly put forward as the target population of the ‘Title 107’ reform, how they experience the accessibility of mental health care has not been investigated since the start of the reform. Addressing challenges in mental health care innovation necessitates bottom-up collaborative (research) practices actively involving (persons with) lived experience ( 23 , 24 ). This study aims to take a first step in this direction by investigating how persons with SUD, regardless of whether they have co-occurring mental health issues, experience the accessibility of mental health care services in the context of the Belgian reform.

2.1 Methodological approach

This study aimed to gain insight into the accessibility of mental health care for persons with SUD, by focusing on service users’ experiences. A qualitative methodological framework was employed, and data were collected using in-depth interviews and were analyzed through thematic analysis.

2.2 Research location and participants

Participants were recruited from five different mental health networks in Belgium (3 in Flanders, 1 in Brussels and 1 in Wallonia), as depicted in Figure 1 . The following inclusion criteria were applied: (1) being at least 18 years of age, (2) having self-reported current or past support needs related to problematic substance use and mental health issues, and (3) being proficient in Dutch or French. The primary focus of this qualitative study was on the accessibility of mental health care for persons with SUD, regardless of whether they have co-occurring mental health issues. While individuals with dual diagnoses may have been included, they were not specifically or exclusively targeted. Intending to obtain a diverse and inclusive sample, the sole exclusion criterion was the presence of acute symptoms of mental illness (e.g. psychotic episode) or withdrawal, and persons who were significantly under the influence of substances at the time of data collection. The invitation for participation was shared with service users by staff members working at mental health care services involved in the five selected mental health care networks (Namur, Brussels, South-West-Flanders, Aalst-Dendermonde-Sint-Niklaas and Antwerp). In the recruitment process, we aimed to obtain diversity regarding gender, age and the extent to which substance use problems impacted several life domains. We also aimed to maximize diversity regarding the type of service used when interviewed, based on the five care functions defined in the mental health reform. Besides active service users, we also recruited persons with SUD who were not followed up by any service to understand their reasons for dropping out. We aimed to reach these persons through low-threshold services (e.g. night shelters, street work services and frontline social services) and snowball sampling. For this specific population, we applied an additional inclusion criterion of not having had contact with mental health or specialized addiction services in the past three years. A single overnight stay in a psychiatric ward was not considered an exclusion criterion. Persons with SUD followed up by low-threshold services could be reached relatively easily because the researchers embedded themselves in the operational functioning of these settings for several days to build trust with the target group. Participants appeared willing to recount their stories, feeling that their experiences were finally being heard. Recruitment of participants from other settings also proceeded smoothly, with participants indicating the importance of the accessibility of appropriate care.

Figure 1 Included mental health networks. Dienst Psychosociale Gezondheidszorg. (2020). https://www.psy107.be/ .

This recruitment strategy led to the inclusion of 52 participants across five mental health networks: 8 in Namur, 11 in Brussels, 9 in South-West-Flanders, 14 in Aalst-Dendermonde-Sint-Niklaas, and 10 in Antwerp. Table 1 provides an overview of participant characteristics. We have opted not to include specific data on the mental health issues of participants due to this study’s focus on exploring the accessibility of mental health care for persons with SUD, irrespective of co-occurring mental health issues. In doing so, we intended to highlight the heterogeneous and multifaceted experiences and needs of service users with SUD, rather than categorizing them by specific psychiatric problems or diagnoses.

Table 1 Participant characteristics.

2.3 Data collection and analysis

Semi-structured in-depth interviews ( 25 ) were guided by an interview schedule focused on participants’ experiences regarding their (history of) substance use, past and current use of services, (un)met support needs and helping and hindering factors regarding the accessibility of services. While some participants described their experiences openly, sharing their feelings and emotions, others provided more factual and practical responses, requiring further probing. Interviews (n=52) lasted approximately one hour and were audio-recorded, transcribed verbatim, and analyzed through an inductive thematic approach ( 26 ). In the first analysis phase, a subset of seven key interviews was selected based on the richness and diversity of the experiences they captured. The first author (CDR) then conducted an in-depth analysis of each key interview, becoming familiar with the data and assigning initial codes and generating initial themes, represented visually as a mind map with emerging and interconnected superordinate themes ( 27 ). This initial thematic structure was discussed in-depth with co-researchers JM, IG, and MC to refine potential themes and sub-themes. Based on this, themes were defined and named ( 26 , 27 ). In the second analytical phase, this thematic structure was used as a guiding framework for analyzing the remaining interview data, leading to a fine-grained analysis of participants’ experiences. This was then discussed and finalized by the entire research team.

2.4 Ethical considerations

This study was approved by the Ethics Committee of Ghent University Hospital (reference number B4032021000133). Participants provided written informed consent before participation in the study and received a 20 euro supermarket voucher as compensation.

Despite the adapted network structure following the ‘Title 107’ reform, participants still experienced mental health care services as isolated ‘islands in the stream’. This metaphor aptly captures how participants still experienced mental health services as separate and distinct entities despite the reform’s ambition to realize more intensive collaboration and greater cohesion between and across services in generic mental health care and specialized SUD services. In contrast, participants reported encountering several challenges in accessing and navigating these loose networks. More precisely, five main themes emerged from the data: (a) fragmentation of care and support, (b) (lack of) “really listening”, (c) balancing between treatment-driven and person-centered support, (d) the ambivalent role of peers, and (e) stigma. Within each theme, we captured a variety of experiences and ambivalences, confirming the idiosyncratic nature of participants’ needs. We applied a dynamic lens to the facilitators and barriers affecting the accessibility of mental health care for persons with SUD. We did not distinguish between generic mental health services and specialized SUD services in our analysis due to the participants’ heterogeneous treatment experiences. This approach aimed to reflect the complexities and ambiguities in their narratives, thereby highlighting the diverse recovery trajectories within the SUD population. The distinguished sub-themes within each theme aim to capture these ambiguous dynamics.

3.1 Fragmentation of care and support

Participants experienced mental health care as a fragmented and dispersed field that is challenging to navigate, influenced by different aspects: the ripple effect of waiting lists and so-called ‘island logic’ within a network structure.

3.1.1 The ripple effect of waiting times

It is a long-standing fact that waiting times are a structural barrier to accessing appropriate services, both within the generic mental health care and specialized addiction treatment system. Waiting times are highly variable and can differ significantly between treatment settings, mental health care regions, and periods. The experiences of the participants allowed us to look beyond this systemic reality and gain an understanding of the rippling effects waiting times caused in the recovery processes of persons with SUD. Several participants explained how the momentum and motivation to seek support lie in moments of crisis, when they have hit rock bottom in one or several life domains. Finding oneself on a waiting list in such a moment of crisis can enhance feelings of desperation and lead to dangerous situations. Participants spoke about how waiting times jeopardized their health because they felt completely alone when physically weaning themselves off drugs:

“The waiting lists are the hardest. You want to quit in that moment. You’ve had enough, you want to stop. But if you then have to wait for three months, then you won’t stop. I tried once to quit on my own, but I ended up in the emergency ward and was nearly dead. So, that wasn’t a good idea. I can only quit using with a lot of support.” (male, age 40-50)

Often, the more specialized and long-term the support provided within a certain service, the longer the waiting time. Especially (long-term) residential support proved to be in high demand. A consequence of these waiting times is that other mental health services, designed to provide ad hoc and short-term support, are increasingly used by persons with SUD to “patch up” the gaps created by waiting times in specialized settings. This was particularly the case for psychiatric wards in general hospitals, where the duration of admission is usually limited to up to four weeks. One participant explained how fortunate he was that the generic ward he was admitted to used its discretionary space to allow him to stay for four months:

“Actually I stayed there [psychiatric ward of a general hospital] for so long [4 months] because I was on a waiting list here and I was scared that if I would go home, I wouldn’t make it back here. I used it as a patch-up. Because the year before, they had also suggested to follow a long treatment program and then I went home and didn’t make it. (female, age unknown)

Because psychiatric wards in general hospitals primarily fulfill the function of being a short-term pit stop in space and time, the focus often does not lie on the long-term recovery trajectories of service users, albeit through treatment orientation elsewhere. Moreover, staff are often not specifically trained in supporting persons with SUD. Waiting times also result in adequate support, when it is finally available, no longer being in sync with the recovery trajectories of service users. That is, when support finally becomes available, it risks being mismatched with one’s support needs at that specific time, considerably reducing the chances of a helpful treatment trajectory:

“I’m on the waiting list for seven months now, which is way too long actually. Because you call when you feel bad, not when you feel good. Actually I was okay again already, in terms of my psychosis. Actually I was at work again. And suddenly they called: you can come in. So I take that opportunity because I believe in [facility]. A dual diagnosis ward, not many instances have that. But the waiting list is just terribly long and I can well imagine that many people … drop out.” (male, age 30-40)

Rather than using support modalities that are the best fit with their personal needs and understanding and stage of recovery, waiting times force service users to accept the first available service, whether this is located within generic mental health or specialized addiction treatment services.

3.1.2 Island logic within a network structure

Mental health services are expected to actively provide treatment orientation to partners within their regional network, either as follow-up after treatment or when they cannot provide the most appropriate support themselves (e.g. due to treatment focus, waiting times, black lists). However, participants were often not adequately referred at crucial moments in their recovery process. This contributed to the fragmentation of care trajectories, a lack of motivation, and vicious cycles of problematic substance use. For example, for one participant, fragmented short psychiatric admissions became an inherent part of his recurrent pattern of problematic alcohol use:

“What do you think of psychiatric wards in general hospitals? “That goes really smoothly. Yeah. That is … In less than a week you’re in. But you’re also out quickly again. It is maximum 10 days. (…) It is emergency detox.” And was that helpful to you? “Yeah, you are rid of those withdrawal symptoms for a while.” So when you leave again, do they send you to the social service? “Just back home.” And what did you do at home? “Just start drinking again. (…) That’s how it works, you are sent from one thing to another.” (male, age 40-50)

It is not clear whether these experiences of inadequate referral specifically apply to service users with SUD, or if they point to a more general bottleneck in the ways services collaborate within regional mental health networks. However, the findings affirm that collaboration is essential for continuity of support and how certain services, despite being embedded within a network structure, still apply ‘island logic’ to their daily practice. This bottleneck was also mentioned concerning the bridge between frontline workers and more specialized services. The lack of information and treatment orientation can be demotivating:

“General practitioner? I did not know that system. She didn’t explain anything to me. She just sent me away. (…) She didn’t give me that information. And I really hope that changes in the future. That they don’t just send people away. You see? That is such a shame. Because I was open to recovery. It’s not like they had to force me or so. It’s not like I go there and start making a fuzz. I am open to recovery and still, it’s denied to me. That’s strange.” (female, 40-50)

One participant explained how, after being refused a service he approached, there was a failure to discuss other possible support avenues:

“Then we called [specialized inpatient ward] together to be admitted as a couple. But … We are both in the red there. (…) And they said, ‘ah no, you have been here three times already, I don’t think our way of working works for you. So find another place’. That was it. Not like, go there or go there. No, it was just, no, it’s not going to work here, find somewhere else. Go look on the internet, they said.” (male, age 40-50)

This lack of information was a recurring obstacle in the care trajectories of several participants, who for example described the multitude of treatment options as overwhelming:

“It is just the same with mental health care, there are so many options. But you just don’t know how … You find yourself in some kind of … In a kind of thing that you’ve never … Like a new chapter that you know nothing about, you see? First, you need to know what your rights are and then you can achieve a lot. But you don’t know, you just don’t know.” (female, age 20-30)

Other participants chose not to disclose their substance use-related support needs to their frontline worker (e.g. general practitioner), who could have made referrals to appropriate services within their regional mental health network. Instead, they felt alone in their search for a possible entry point to the mental health care system. One participant explained how he put himself on several waiting lists, based on an elaborate internet search with a friend:

“Yeah, it’s also because of my best friend that I made it here or that I found this treatment place. So she really … We sat in front of the computer together to look up every kind of organization and to call them and … To look what’s the best option. We then made pros and cons, like that organization is better at that, this one is better at that, and then compared. And then decided what to do, what suits me best. She’s a really good friend.” (male, age 20-30)

Another participant believed there was a missed opportunity to spread information to a wider audience regarding support options for persons with SUD. He attributed this lack of information to societal stigma towards SUD:

“You really need to look, on the internet and so on. We had to search really hard … Yeah, you don’t easily find it, support. We really had to look for it. It isn’t addressed enough. I find it should be on the news. Like they show the suicide helpline on the news, it can also be about those kind of things I find. Or on TV or … Like, if you have problems with drugs, this is where you can go.” (male, age 20-30)

However, at the same time, several participants described how a specific professional played an indispensable role throughout their care and recovery trajectories. These professionals provided tailored information, clarified treatment options, ensured consistency and coherence in treatment choices, were reachable both during and outside of crises, and functioned as gatekeepers. Some participants reported that their psychiatrist or general practitioner fulfilled this positive key role, opening doors to new treatment options and guiding toward settings tailored to their needs:

“[My general practitioner] is the only one who knows my whole file. (…) She knew my situation at home, she knew my three children, she knew about the problems with my youngest son, the forced admission, the drug problems, everything. (…) She knew the situation.” (female, age 50-60)

Participants stressed the necessity of someone taking up the role of case manager throughout their support trajectories. However, describing these actors only as case managers might not do justice to the relationships they build with service users. The enthusiastic and passionate tone participants used when talking about these professionals shows how, above all, relational continuity and person-centeredness lie at the heart of these pivotal relationships. One participant described how the continuous proximity and effort of the social worker handling his case gave him a deep feeling of being worthy of care, which was the decisive factor in accepting specialized support:

“The switch came because that staff member from the social service, that woman who you just saw, she stayed on me. And she signed me up to … How do you call that? Forced admission. She just pulled me out from the pit and put me in forced admission. That’s what really woke me up. That woman cared so much about me to save me like that. And yeah, that was the decisive thing for me, like this is enough, I want to step out. (…) The things she explains to me, I actually should have learned from my parents and from school.” (female, age 40-50)

3.2 (Lack of) “really listening”

Related to the above, relational continuity came to the fore as an essential aspect in navigating the mental health care landscape in search of appropriate support. Strikingly, participants often described it as “really listening”, emphasizing the importance of authentic therapeutic relationships and trauma-sensitive care.

3.2.1 Authentic therapeutic relationships

The importance of authentic contact with professionals was prominent in participants’ stories and was a determining factor in the success or failure of services used. For one participant, “the human aspect” was more fundamental than personal comfort or the therapeutic program:

“I have to admit that when I came to [name service] and saw the facilities there, I thought, I won’t stay here. (…) But eventually, because the human contact was so good there, also from the nurses… (…) That’s what made me stay. Their facilities are old-fashioned, not much comfort. Horrible. But the human aspect and the character of caregiving and then the tailored therapists and stuff … They were really good.” (female, age 50-60)

Participants stressed that these relationships should be characterized by sincerity, a non-judgmental atmosphere and a dialogical nature. Participants also foregrounded the value of experiencing a sense of commitment, approachability and trust of professionals with whom they ‘clicked’, as illustrated by a participant describing his bond with a psychiatrist:

“There was one psychiatrist in particular who followed me for several years. She was truly magical. Without knowing it, we followed each other in different hospitals, but each time I found her again and so a bond was created. When I saw her for the first time, she was still an assistant, so it was really … I was kind of her first patient. There was a real bond that had been built up with her, and she was also the first psychiatrist my parents felt comfortable with.” (female, age 20-30)

Relational continuity has a strong enhancing effect over time and across different support settings. However, certain factors prevent such dialogical and authentic relationships from developing. One that stood out was the unequal power dynamics between professionals and service users that are unavoidably at play within treatment settings. This power imbalance was most pronounced in contacts with psychiatrists, in which symptomatology sometimes prevailed and stigmatizing attitudes on substance use shone through:

“I’m dealing with a psychiatrist. Apart from neuroleptics, which I don’t know anything about, I know more than he does about the products they spend all day prescribing. I have more expertise and yet he infantilizes me as a drug addict, even though I have quite a broad expertise.” (female, age 40-50)

The results also showed how lasting therapeutic relationships, for example through long-term outpatient support provided by psychologists, were often jeopardized by a structural financial barrier to the continuity of affordable support. Several participants recalled positive and impactful experiences regarding their relationship with their psychologist. At the same time, some participants explained how they would benefit from continued long-term regular contact with their psychologist, but simply could not afford it:

“I know I need help for the rest of my life. But I also know that I need to pay for it myself. (…) If I say I need my therapist once every three weeks in order to keep functioning, then I should do that. But if I need to pay for it all myself, then I know that one day sooner or later I’ll feel quite good and think I don’t need it anymore. But from the past I know that there will be moments then that I do need it and then it’s too late.” (female, age 50-60)

3.2.2 Trauma-sensitive care and support

Participants often mentioned how authentic therapeutic relationships could only be developed when professionals looked beyond the behavioral aspects of their SUD. Particularly, they referred to the importance of addressing the root causes of their SUD, such as adverse childhood experiences, detrimental social circumstances and trauma. One participant explained how the active acknowledgment of these underlying factors unlocked a new phase in his recovery process:

“Here, in [specialized addiction treatment ward], it was really good to just focus on what it is and then to deal with what’s behind it. (…) Here, I talk with therapist X, and from the start … I hate her. In the sense of … She knows it. She sees through me and she just gets it. (…) Here, I don’t know … Yeah, I really learned how to feel. And that is not easy.” (female, age 20-30)

In some cases, the provided therapeutic activities simply fell short of bringing these underlying dynamics to the fore. One participant reflected on how her traumatic experiences themselves made it impossible for her to take the initiative to talk about them. At the same time, paradoxically, she was aware that addressing them was a necessary part of her recovery process. During her admission, staff members seemed oblivious to this need and the therapeutic activities fell short in eliciting these traumas:

“Now I know, if you have an addiction problem, you need to focus on things in the head. (…) In psychiatry I had the feeling, if I can’t open up myself because I’ve been through so many traumas … You are the professional, you should be able to help me unravel things. That’s what I think now. But back then, it just wasn’t there. I’ve missed that. (…) Nobody could know who I am or what happened to me. And what to do. Especially, what to do to get out. (…) My traumas come up, I’m stuck with them. And they are like a whirlwind storming in my head. And I sit there alone with my thoughts.” (female, age 40-50)

3.3 Balancing between treatment-driven and person-centered support

Another factor that significantly impacts mental health care accessibility is the extent to which service users experience a good fit between their personal support needs and what certain services have to offer. In this respect, three dimensions stood out: the intake criteria used by services, the expertise of staff regarding SUD, and how recovery was operationalized within services.

3.3.1 The (in)flexibility of intake criteria

Both generic mental health and specialized addiction treatment services often target a specific service user profile, translated into intake criteria acting as gatekeepers to the service. Participants reported diverse experiences regarding how freely these criteria were applied. One participant experienced how a high level of inflexibility left no room for real dialogue or a person-centered exploration of his needs during intake:

“They just want to hear what they want to hear. Their book says such and such. You have to do it that way and you have to ask that question and if you get an answer, then you send [the service user] walking. It is as if they have been indoctrinated with their [intake] questionnaire in front of them. And if someone answers differently to that [intake] questionnaire, then they are already at a loss. (…) That’s how it comes across to me anyway. (…) And then just like that [they say] ‘Yes, this doesn’t fit with us and we don’t have time for you, good riddance’.” (male, 50-60)

At times, substance use in itself was an explicit exclusion criterion in generic mental health services. One participant was denied access to sheltered housing, which directly contributed to a cycle of substance use and possible relapse:

“I’ve still been turned down for sheltered housing because they know I’m from [service name] and that I’m a consumer. They told me it’s not going to be possible.” And what solutions did they propose? “Follow-up for drug use. Basically, you have to stay on the street and monitor your drug use. It’s a bit complicated though. (…) Because when you have a roof over your head, it’s easier to stop using or to set up a follow-up system. When you’re on the street, what do you do? You just want to use because you’re not feeling well.” (male, age 40-50)

Other participants experienced how some mental health services denied access to persons receiving (opioid) substitution treatment. Whereas this might be related to the service’s perceiving it as a transgression of its substance use policy, it was often described as stigmatizing. For one participant, this barrier significantly obstructed his recovery process:

“I would just like to get in somewhere. (…) And then I’m 100% sure that I can hold on for another year. Or longer. And preferably for the rest of my life, my liver isn’t doing so well anymore. (…) But I’m telling you, in those [generic short-term mental health wards], because I take Suboxone…” That makes that you don’t get access to several services that could help you? “Not a single one. (…) As soon as you mention that you take Suboxone or you’re in a drug rush … No, then you can go home.” (male, age 40-50)

Support modalities rooted in a harm reduction approach and more treatment-oriented support could simultaneously play a valuable role in one’s recovery process. However, from a service intake perspective, these seem to mutually exclude each other.

3.3.2 Training and expertise of staff

Participants’ feeling of having ended up in the ‘right’ or ‘wrong’ kind of treatment was also dependent on the extent to which care professionals had specific expertise regarding SUD. Insufficient training about SUD among staff members enabled some participants to hide or ‘separate’ their SUD, which had differing effects. On the one hand, hiding their substance use problems increased access to generic mental health services. On the other hand, the support they received in these places was not sufficiently tailored to their specific needs. At times, this ‘separation’ strategy even led to misdiagnosis:

“I came out [of the psychiatric ward] more addicted than when I started. Because actually I was there for the wrong reasons. (…) They also said that they didn’t focus on drugs, so I just kept on using. I came in under influence, they didn’t even notice. That’s really bad, but I shouldn’t joke about it. (…) And also, the psychiatrist there, I found it so striking, because … They gave me diagnoses that actually didn’t apply at all. For example, bipolar disorder. Eventually it all turned out not to be true, but I did get medication [benzodiazepines] for it.” (female, age 20-30)

Some participants also reported that staff members in generic mental health care were at times not sensitive enough towards the addiction-related vulnerabilities they experienced regarding prescription medication. Other participants reported that frontline healthcare and social professionals (e.g. general practitioners and social workers) were insufficiently aware of how their attitudes and actions might have a directly negative effect on their recovery process. For example, for one participant, failure to keep an appointment with the social worker was used as a reason for the withdrawal of social benefits, which significantly worsened her substance use problems and led to a feeling of not being supported:

“[The social worker] already docked my pay twice because I didn’t keep an appointment. But when you’re in this (substance use), sometimes you forget the days, so you’re already in a bad way and they dock your pay twice a year.” (female, age 50-60)

Participants often experienced a greater sense of belonging and a better alignment with their long-term and recovery-oriented support needs if services were specialized for persons with SUD (and co-occurring mental health problems).

3.3.3 Operationalizations of recovery

Participants valued a good fit between their understanding of recovery and the way recovery was operationalized in the service they used. In particular, the extent to which abstinence is considered a core condition of recovery was often stressed. For example, one participant saw abstinence as the fundamental starting point of her recovery trajectory. Ending up in a women’s group where using substances was tolerated, was not well-aligned with her vision of recovery:

“I’m in a women’s group … An ex-addicted women’s group. It’s a women’s group for women of the street. But using is allowed there. So the idea is to allow people who use and to support them like that. But I want to quit completely. So I want to take some distance of that women’s group, because when I go there, I see those people stoned. That weighs heavily on me.” (female, age 40-50)

In the same vein, some participants experienced they could not work on their recovery trajectory in services where recovery was operationalized through a strict (hierarchic) structure with many rules. Another recovery-related influencing factor was the extent to which services provide support in all life domains, not just the clinical and functional aspects of recovery. One participant received help with his social problems during admission, which exceeded his expectations and positively impacted retention:

“I immediately noticed how the social department was involved to find out how they could help me. I didn’t have a health insurance, I had nothing. Nothing. And they immediately tried to support me in all these aspects … And then it was continued here in [residential ward], also with the social department and … They really supported me and helped me find solutions. Something I hadn’t expected. I thought, I’m here now and I’ll get sober and I’ll be on my own for everything else. But that wasn’t the case.” (female, age unknown)

3.4 The ambivalent role of peers

Peers played a unique and influential role in facilitating access to services, both through their formal presence in services (e.g. as peer workers and service users) and informally.

3.4.1 Identification with peers

Several participants talked about how the presence of peer workers in mental health services was supportive and motivating, as they were assigned a special position with a positive influence. This was mainly attributed to the fact that peer workers, because of their experiential expertise, were able to understand what they were going through and did not have a judgmental attitude towards substance use.

“[The peer worker] sees through you. (…) She just really knows it, she really knows it. She can really look at you and whatever you say, she can really laugh and then inside you feel like ‘oh fuck, she got me’. The staff is good to support you, but peer workers are good to really give you insight. Because also, you just believe them, they know what they’re talking about.” (female, age 20-30)

Another important aspect is the extent to which participants identify with the service user population in available services. While some participants have difficulties identifying with the label of having mental health problems, others would rather be associated with mental health services than specialized addiction treatment services. For some participants, encountering persons with (severe) mental health problems in mental health services had an estranging and even traumatic effect. Other participants mentioned how fellow service users can contribute to feelings of belonging and safety within treatment settings, positively affecting retention. At the same time, participants reported how a lack of identification with the mental health problems or lifeworld of fellow service users can cause feelings of unsafety, leading to drop-out or even a priori avoidance of these services.

3.4.2 Word of mouth

Together with the presence of peers and peer workers in mental health care, it became clear how the informal influence of peers was even stronger. Several participants mentioned the role of peers in their own near (e.g. family or close friends) or distant (e.g. people from the same neighborhood) social network who had lived experience with generic mental health care and/or specialized substance use treatment. Informally sharing these experiences between peers appears to be common and acts as a powerful testimony, placing services in an attractive or unattractive light depending on the experiences. Additionally, for some participants, this insider information functioned as the primary source of information regarding the daily practice, characteristics, and approach of services, based on which participants decided whether or not to use the service.

“I’ve been in other admissions where I was together with people who had been in [residential specialized service]. And [residential specialized service] has got a really strong regime. Actually, I was allowed there, but I refused it.” Do you think it wouldn’t be for you? In an [residential specialized service] ward or admission? “Of course it would. Because I have an addiction too. (…) But those rules … You can’t have your phone. You can’t have that. I’ve only heard this from other people of course. But those people have been there so they won’t lie about it.” (male, age 30-40)

The above insights illustrate how peers play an ambiguous role in the accessibility of mental health care services for persons with SUD.

Stigma was a recurrent theme throughout the interviews and is entangled with other themes. Three different stigma-related dimensions were distinguished: stigma within mental health care, ambivalence towards labels and stigma within people’s social networks.

3.5.1 Stigma within support and care

Stigma is subtly present within the mental health care system itself, having diverse effects on how participants experience and use available services. Participants’ accounts showed how stigma comes to the fore in multifaceted ways, such as judgmental attitudes, language use, preconceived approaches to treatment planning, and engrained institutional logic. Several participants had mixed experiences with psychologists, especially in outpatient (private practice) settings. Whilst some participants found a lasting and supportive connection with their psychologist, others spoke about how perceived stigma and stereotypical perspectives hampered relational continuity and the possibility of openly talking about substance use. Such relational dynamics might even trigger or reinforce feelings of shame:

“I’ve had certain psychologists who … With whom I felt judged. It was just a kind of vibe of … I had the feeling that they thought ‘yeah yeah, it’s no good…’ And when I had drunk, I made stupid mistakes, adultery, things I would never do when sober so I felt a bit judged. I also tried several ones.” (female, age 30-40)

In certain mental health care settings, the narrow idea of recovery as a linear and abstinence-based process was still dominant. In reality, the recovery processes of service users with SUD often have an unpredictable and slow course, inherently characterized by ups and downs and relapse, challenging the attainment of this abstinence-based norm. Furthermore, anticipated stigma prevented participants from opening up about their SUD to frontline workers. Some participants had developed strategies to compartmentalize these support needs, as in this interaction with a counselor:

“They help me with my social benefits. And yeah, I can always talk to them if something’s wrong. But like [my friend] just said, not about drugs. That’s just for the MSOC. (…) Because I want to keep that separate. (…) I have the feeling they would look at me differently then. Yeah, it’s just a feeling. (…) They would automatically behave differently towards us than we’re used to. Automatically. Whether they want to or not, they would do so.” (male, 50-60)

3.5.2 Ambivalence towards labels

Participants had ambiguous relationships with psychiatric and substance use-related labels. Some participants struggled to identify themselves as someone with an SUD and rather considered themselves as someone with mental health problems. Whilst this reluctance to associate themselves with their problematic substance use in favor of a psychiatric diagnosis lowered the threshold to generic mental health services, it was often rooted in dynamics of self-stigma:

“I never labeled myself as an alcoholic. You can’t tell me I’m an alcoholic. So, I don’t agree with that. Well, I know I am, but I don’t want to know.” (female, age 50-60)

For some participants, it was not so much self-stigma that was at play, but rather their stereotypical ideas about persons with SUD that seemed too far removed from their own lived experiences:

“I find it a difficult topic. I don’t want to be ‘the addict’. In my head I still see an addict as someone sitting in a squat with a needle in their arm, lying on the ground. And it’s not like that at all. I always kept on working, I never had unemployment benefits, I had benefits for just two months. I’ve always worked and I’ve always used. I have a daughter, I also didn’t use in front of her.” (male, age 30-40)

Another participant expressed how he experienced the medical-social center (i.e. a place for harm reduction support) as a risky place to hang out, because of the presence of other persons with SUD:

“Yeah, they are willing to steal from you there. And many of them quietly come and get their medication. But more than half of them come there to do criminal activities. And people like me are easy to rip off. See?” (female, age 40-50)

Other participants had opposing experiences with labels, as they identified themselves as someone with SUD but preferred not to be associated with psychiatric labels. These perspectives were colored by stereotypical ideas about the daily practice of mental health services, raising the threshold to using services situated within the ‘psychiatric’ support landscape. This possibly points to a lack of (access to) correct information about mental health care for persons with SUD:

“Everything related to psychiatry and … I see like … yeah … crazy people. So I can’t imagine that I would do that. And I never had a depression before in my life.” (male, age 30-40)

From a care perspective, labels open doors to specific forms of professional support that might be able to offer person-centered care tailored to one’s needs. From a service user perspective, however, stigma in all its forms has a powerful threshold-raising effect. The notion that once you get a label, you can never get rid of it, also shone through.

3.5.3 Stigma within the own social network

Participants also spoke about the hampering effect of stigmatizing perceptions of SUD and/or mental health problems within their social network:

“Like my mother, she thinks it’s a crazy house here, while there are normal people here, like you and me. (…) It was especially difficult, for my work and family, to say ‘this is what’s going on’. I then also said ‘I have psychosis’. Because I find it sounds less bad than ‘I have an addiction’. (…) People have a really bad idea of what addiction is. Or a mental illness.” (male, age 30-40)

At the wider community level, stigma also influenced participants’ decision-making processes in seeking access to support. One participant witnessed how community gossip was set in motion after her dentist sought help for his problematic alcohol use, shaping her decision not to use specialized addiction treatment herself:

“The day you say ‘I quit’, that’s when they look at you. ‘Ah yeah, she drinks’. That’s when you get a finger pointing at you from those people. I saw it happening to our dentist, how they treat him. He’s a drunk. But we were all equally big drunks, but he gets that label. That’s why I don’t want to go to an addiction ward.” (female, age 50-60)

4 Discussion

This study aimed to discern the lived experiences of persons with SUD regarding the accessibility of mental health care in Belgium. Despite the ‘Title 107’ nationwide mental health reform towards more collaboration and de-categorization, participants still experienced mental health care services as ‘islands in the stream’ within the reformed network structure. Just as islands may vary in size and resources, mental health services differ in terms of accessibility, expertise regarding SUD, the vision of recovery, proximity to other ‘islands’, and infrastructure, amongst other aspects. Participants reported feeling lost within these loose networks, struggling to access the right services at the right time and tailored to their specific substance use-related needs. Below, we address several critical challenges that should be prioritized in future research and policy development to enhance the accessibility of mental health care for persons with SUD.

4.1 Breaking the vicious cycles of waiting times

Waiting times jeopardize the accessibility of mental health care for persons with SUD in more complex ways than just ‘standing in line’ for appropriate support. They cause a clogged-up system in which, on the one hand, persons with SUD are not able to access the most appropriate services when they need them. On the other hand, persons who have endured lengthy waiting periods may occupy spaces that are not aligned with their current needs, driven by a sense of desperation to secure any available spot. To unclog these dynamics, it is helpful to build on the recently developed ecosystem theory of mental health care (‘Ecosysteem Mentale Gezondheid’) shaping current mental health care innovations in the Netherlands ( 28 – 31 ). Central to this ecosystem theory is how in well-functioning mental health ecosystems, all involved services and actors have specific characteristics and expertise and fulfill unique and complementary roles. The strength of the ecosystem as a whole thus depends on the extent to which services can take on their core role. However, as described above, lengthy waiting times affect and change the services’ daily practices, put considerable pressure on (the possibility of) symbiotic collaborations, and disrupt the homeostasis of ecosystems, resulting in diffuse networks that are hard to navigate for service users. Moreover, a recent study by Williams and Bretteville-Jensen (2022) revealed how lengthy waiting times have a detrimental impact on service users’ psychological and physical health, have adverse effects on social functioning, heavily jeopardize recovery processes, lead to lower motivation to engage in treatment and result in overall greater severity of illness upon entry to the mental health care system ( 32 ). In that respect, one of the central propositions worth adopting from the ecosystem’s vision of mental health care is to avoid that service users, influenced by the way mental health care is organized, perceive one singular treatment modality as perfectly aligned with their support needs and thus worth waiting for. Instead, offering and actively promoting a diverse array of options is crucial, built on the premise that other treatment options might present equally viable alternatives that are immediately available, devoid of waiting times. From that perspective, the key to a well-functioning mental health care system lies in offering recurrent options rather than in focusing on one-time interventions, acknowledging that sustained success is not magically guaranteed. At the same time, tackling (the ripple effects of) waiting times remains a wicked problem that requires urgent action from high-level actors across several policy domains, transcending the level of individual services and even the level of mental health networks as a whole.

4.2 Organizing relational case management

Positive experiences of participants were almost always related to the continuous support of a key figure (e.g. general practitioner, psychiatrist, social counselor) across different services and stages of recovery, providing person-centered support (“they know me”), strengthening relational continuity and informally taking on the role of case manager ( 33 ). In the original ‘Title 107’ blueprint, the principle of case management was foreseen to be the responsibility of the mobile teams. This idea is in line with international de-institutionalization trends, in which case management has generally been allocated to Assertive Community Treatment (ACT) ( 34 , 35 ). However, thus far, it has not been fully or structurally operationalized, as the ACT model has not been evenly rolled out in all the networks. As a result, several mobile teams do not work according to ACT principles. Moreover, several mobile teams are reluctant to support persons with SUD or to include a professional with substance use-specific expertise in their team. Alongside these operational flaws, a more fundamental question that arises is whether it is possible to structurally roll out a form of case management that guarantees relational continuity for all service users (e.g. by appointing each service user to a case manager). Another question to address is whether it is desirable to organize case management as a separate profession within the mental health care networks. The positive key actors in the participants’ accounts were always actively involved in actual care provision and considered case management to be an inherent part of their job. In short, while providing relational continuity can contribute to the accessibility of mental health care for persons with SUD, we believe this should be a collective responsibility of the network, instead of being allocated to individual case managers. They might risk being burdened with the challenging and unattainable duty of both bridging between different service providers in a fragmented care landscape and providing relational continuity to service users. Such a team-based approach could improve continuity of care and facilitate shared decision-making responsibilities ( 36 ), which may diminish the risk of burnout among staff ( 37 ). On the other hand, such an approach might increase the complexity of organizing care coordination and communication in a complex healthcare system ( 36 ). While case management has been proven to strengthen treatment linking and retention for persons with SUD ( 38 ), research has also shown that implementing case management is in itself no guarantee of better relational continuity ( 39 , 40 ). However, to strive for maximization of relational continuity and case management for all service users, and particularly for persons with SUD, the Belgian system might benefit from structurally integrating a Flexible Assertive Community Treatment (FACT) approach, in which principles of flexibility and continuity are combined to ensure that support is person-centered and to prevent service users from being transferred to different teams when their level of needs change ( 41 ).

4.3 Tackling stigma and centralizing lived experience

Persons with SUD are among the most stigmatized groups in society. There is ample research showing that this stigma significantly interferes with help-seeking behavior in complex and far-reaching ways ( 42 ). Alarmingly, our study affirms how stigma also carries over into mental health service provision through judgmental attitudes and language used by service providers and through institutionalized practices and policies, causing iatrogenic harm. Following the Convention on the Rights of Persons with Disabilities, using SUD as an exclusion criterion to generic mental health care undermines the safeguarding of service users’ human rights, as such vulnerabilities should never be an incentive for exclusion from regular care ( 43 ). To enhance the accessibility of mental health care for persons with SUD, actively challenging and counteracting engrained stereotypical ideas and stigmatizing practices within mental health services is of utmost priority ( 44 ). In that respect, our findings point towards two realistic frontiers. The first challenge relates to the ways psychiatric diagnoses and substance use-related labels function as gatekeepers or barriers to mental health services. Our study showed how service users relate to these labels in highly ambivalent ways, often due to self-stigma, impacting the accessibility of mental health care services ( 45 ). While diagnostic labels can facilitate access to services, they can also have the adverse effect of raising the threshold of the same services, as service users are required to actively and openly identify with these labels to gain entry. These struggles often remain under the radar of service providers but have a profound effect on how service users navigate their care trajectories. A greater sensitivity of frontline workers and mental health care providers towards these ambiguous relationships with labels is warranted. In that respect, both sensitivity training aimed at reducing stigma and specific training of service providers regarding SUD might enhance their confidence in working with persons with SUD and would lead to better health outcomes for service users. A second frontier relates to the fact that, despite the increasing deployment of peer workers in mental health care, the representation of peer workers with lived experience of SUD in generic mental health care services remains low ( 46 ). Involving peer workers in service delivery has an empowering effect, as it helps service users overcome self-stigma and foster feelings of hope ( 47 ). At the same time, peer workers contribute important expertise regarding (recovery from) SUD to mental health teams, provided that they are given an equal position of “partners in co-creation” of recovery-oriented support ( 48 ).

4.4 Fostering recovery-promoting collaborations

Recovery is often put forward as a bridging framework to foster collaborations between generic mental health care and specialized addiction treatment services, especially in favor of persons with co-occurring mental health issues and SUD ( 49 ). Additionally, there is consensus that recovery processes are highly idiosyncratic in nature and are defined in multiple and multidimensional ways (e.g. abstinence, improved health and well-being, taking up socially valued roles), translated into various possible pathways to recovery ( 8 ). However, our study demonstrates how generic mental health care services often continue to endorse narrow views of addiction recovery, promoting sustained abstinence as the only viable recovery pathway. Such narrow views do not bridge but instead divide the mental health care landscape, as they feed into the assumption that substance use problems are the fundamental issue that needs to be tackled before mental health can be addressed, an outdated sequential treatment concept deemed ineffective and failing to recognize the importance of addressing trauma in supporting persons with SUD ( 50 , 51 ). In contrast, persons in recovery benefit from integrated treatment systems in which different types of generic and specialized support are (simultaneously) accessible at different points in their recovery process, aligned with their evolving support needs ( 52 ). Earlier work underscores the importance of promptly accessible integrated treatment services for addressing mental health and substance use ( 53 ). This contrasts with the complex, fractured systems and services operating in silos so frequently encountered by service users ( 54 ). To operationalize such integrative mental health care systems, more productive collaboration between frontline, generic, and specialized services needs to be fostered. Integrated care calls for a fundamental shift towards shared decision-making between all parties involved, including persons with mental health and substance use concerns ( 54 ). A cornerstone of service delivery is the concept of ‘no wrong door’, referring here to the delivery of care beyond a specific organization’s boundaries, and facilitating access to other substance use, mental health or other services to ensure all needs are met ( 55 ). To operationalize such integrative mental health care systems, more productive collaboration between frontline, generic and specialized services needs to be fostered. This can only be attained through a shared vision of mental health and addiction recovery, in which nuanced and multifaceted meanings of recovery are adopted. Only by actively promoting multiple pathways to recovery (e.g. non-abstinent recovery, controlled use, abstinence-based recovery) can recovery truly act as a bridging philosophy between sectors, enabling more adequate referrals, co-development of support trajectories and a continuous exchange of expertise, thus significantly lowering barriers to adequate support ( 7 , 8 , 56 ).

4.5 Limitations

Several limitations of this study should be taken into account. The first set of limitations is related to the use of convenience sampling for participant recruitment, which may have caused selection bias. Although the five included mental health care networks were chosen based on reaching maximal diversity, they may not be fully representative of all 20 Belgian mental health care networks. Additionally, although a concerted effort was made to include persons with SUD who were not in contact with services, they remained a minority. In future research, convenience sampling should be complemented with other sampling techniques to reach and include the experiences of persons with SUD in highly precarious situations. Second, as this study is situated within the specific Belgian mental health care context, shaped by specific local social, cultural and political dynamics, results should be generalized with care to other international mental health care contexts.

5 Conclusion

To transform mental health care networks from ‘islands in the stream’ to more cohesive and collaborative ecosystems, the above-described critical points should be seen as priority areas to be addressed in further research and policy development. Before concluding this article, a critical limitation of this study must be emphasized. One of the goals was to centralize the lived experiences of persons with SUD, as we problematized they often remain overlooked in research evaluating the effects of mental health reforms. The chosen qualitative methodology enabled us to build an understanding of how macro-level developments affect the micro-level lives and recovery processes of persons with SUD. However, to advance the mental health care field, we need to take a step further and move away from top-down policy development and mental health care design. Instead, future mental health care innovation and design should be built on co-productive research approaches that rely on persons with SUD as fully equal partners and decision-makers ( 24 , 57 ).

Overall, the challenges described in this study do not only specifically relate to the accessibility of mental health care for persons with SUD, but are also symptomatic of underlying flaws of the Belgian mental health reform affecting all service users. In that respect, in striving towards well-functioning mental health care ecosystems, persons with SUD should not be treated as a separate or especially complex category of service users, but as a heterogeneous group with equally diverse needs and visions of recovery as all other service users. We hope that our analysis and recommendations lead to actions that positively impact mental health care delivery for all service users, not least for persons with SUD.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Ethical Committee of Ghent University Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

CD: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. JM: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. IG: Formal analysis, Investigation, Methodology, Writing – review & editing. MC: Formal analysis, Investigation, Methodology, Writing – review & editing. DS: Conceptualization, Writing – review & editing. PD: Conceptualization, Supervision, Writing – review & editing. JD: Conceptualization, Supervision, Writing – review & editing. PN: Conceptualization, Supervision, Writing – review & editing. WV: Conceptualization, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Belgian Science Policy Office (BELSPO), reference number DR/89/SUMHIT.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Degenhardt L, Bharat C, Glantz MD, Sampson NA, Scott K, Lim CC, et al. The epidemiology of drug use disorders cross-nationally: Findings from the WHO’s World Mental Health Surveys. Int J Drug Policy . (2019) 71:103–12. doi: 10.1016/j.drugpo.2019.03.002

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Patel V, Chisholm D, Parikh R, Charlson FJ, Degenhardt L, Dua T, et al. Addressing the burden of mental, neurological, and substance use disorders: Key messages from Disease Control Priorities. Lancet . (2016) 387:1672–85. doi: 10.1016/S0140-6736(15)00390-6

3. Drake RE, Essock SM, Shaner A, Carey KB, Minkoff K, Kola L, et al. Implementing dual diagnosis services for clients with severe mental illness. Psychiatr Services . (2001) 52:469–76. doi: 10.1176/appi.ps.52.4.469

CrossRef Full Text | Google Scholar

4. Carrà G, Bartoli F, Clerici M, El-Guebaly N. Psychopathology of dual diagnosis: New trumpets and old uncertainties. J Psychopathol . (2015) 21:390–9.

Google Scholar

5. Fernández-Miranda JJ, Pascual-Pastor F, Díaz-Fernández S, Navarro D, Manzanares J. Differences in substance use disorders and other mental disorders in mental health and addiction settings: Sociodemographic, clinical, drug treatment, and gender differences. Int J Ment Health Addict . (2022), 1–19. doi: 10.1007/s11469-022-00989-6

6. Vanderplasschen W, Best D. Mechanisms and mediators of addiction recovery. Drugs: Education Prev Policy . (2021) 28:385–8. doi: 10.1080/09687637.2021.1982521

7. Best D, Vanderplasschen W, Van de Mheen D, De Maeyer J, Colman C, Vander Laenen F, et al. REC-PATH (recovery pathways): Overview of a four-country study of pathways to recovery from problematic drug use. Alcoholism Treat Quarterly . (2018) 36:517–29. doi: 10.1080/07347324.2018.1488550

8. Dekkers A, Bellaert L, Meulewaeter F, De Ruysscher C, Vanderplasschen W. Exploring essential components of addiction recovery: A qualitative study across assisted and unassisted recovery pathways. Drugs: Education Prev Policy . (2021) 28:486–95. doi: 10.1080/09687637.2021.1943315

9. Degenhardt L, Glantz M, Evans-Lacko S, Sadikova E, Sampson N, Thornicroft G, et al. Estimating treatment coverage for people with substance use disorders: An analysis of data from the World Mental Health Surveys. World Psychiatry . (2017) 16:299–307. doi: 10.1002/wps.20457

10. Yule AM, Kelly JF. Integrating treatment for co-occurring mental health conditions. Alcohol research: Curr Rev . (2019) 40:PMC6799972. doi: 10.35946/arcr.v40.1.07

11. Savic M, Best D, Manning V, Lubman DI. Strategies to facilitate integrated care for people with alcohol and other drug problems: A systematic review. Subst Abuse Treatment Prevention Policy . (2017) 12:1–12. doi: 10.1186/s13011-017-0104-7

12. Vanderplasschen W, De Bourdeaudhuij I, Van Oost P. Co-ordination and continuity of care in substance abuse treatment: An evaluation study in Belgium. Eur Addict Res . (2002) 8:10–21. doi: 10.1159/000049483

13. Nicaise P, Dubois V, Lorant V. Mental health care delivery system reform in Belgium: The challenge of achieving deinstitutionalisation whilst addressing fragmentation of care at the same time. Health Policy . (2014) 115:120–7. doi: 10.1016/j.healthpol.2014.02.007

14. Gezondheidszorg GG. Gids naar een betere geestelijke gezondheidszorg door de realisatie van zorgcircuits en zorgnetwerken. In: Psy107 . Vlaanderen (2010).

15. Mistiaen PCJ, Detollenaere J, Devriese S, Farfan-Portet MI, Ricour C. Organization of mental health care for adults in Belgium . Brussels: Belgian Health Care Knowledge Centre (KCE (2019). Health Services Research (HSR).

16. Priester MA, Browne T, Iachini A, Clone S, DeHart D, Seay KD. Treatment access barriers and disparities among individuals with co-occurring mental health and substance use disorders: An integrative literature review. J Subst Abuse Treat . (2016) 61:47–59. doi: 10.1016/j.jsat.2015.09.006

17. Mahdanian AA, Laporta M, Drew Bold N, Funk M, Puras D. Human rights in mental healthcare; A review of current global situation. Int Rev Psychiatry . (2023) 35:150–62. doi: 10.1080/09540261.2022.2027348

18. WHO. Mental health, human rights and legislation: Guidance and practice . Geneva: World Health Organization (2023).

19. Lorant V, Grard A, Van Audenhove C, Helmer E, Vanderhaegen J, Nicaise P. Assessment of the priority target group of mental health service networks within a nation-wide reform of adult psychiatry in Belgium. BMC Health Serv Res . (2016) 16:1–9. doi: 10.1186/s12913-016-1434-2

20. Nicaise P, Grard A, Leys M, Van Audenhove C, Lorant V. Key dimensions of collaboration quality in mental health care service networks. J Interprofessional Care . (2021) 35:28–36. doi: 10.1080/13561820.2019.1709425

21. Nicaise P, Giacco D, Soltmann B, Pfennig A, Miglietta E, Lasalvia A, et al. Healthcare system performance in continuity of care for patients with severe mental illness: A comparison of five European countries. Health Policy . (2020) 124:25–36. doi: 10.1016/j.healthpol.2019.11.004

22. Bredewold F, Hermus M, Trappenburg M. ‘Living in the community’ the pros and cons: A systematic literature review of the impact of deinstitutionalisation on people with intellectual and psychiatric disabilities. J Soc Work . (2020) 20:83–116. doi: 10.1177/1468017318793620

23. Stanhope V, Solomon P. Getting to the heart of recovery: Methods for studying recovery and their implications for evidence-based practice. Br J Soc Work . (2008) 38:885–99. doi: 10.1093/bjsw/bcl377

24. Veldmeijer L, Terlouw G, Van Os J, Van Dijk O, Van't Veer J, Boonstra N. The involvement of service users and people with lived experience in mental health care innovation through design: Systematic review. JMIR Ment Health . (2023) 10:e46590. doi: 10.2196/46590

25. Baumbusch J. Semi-structured interviewing in practice-close research. J Specialists Pediatr Nursing . (2010) 15:255–8. doi: 10.1111/j.1744-6155.2010.00243.x

26. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol . (2006) 3:77–101. doi: 10.1191/1478088706qp063oa

27. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qual Res Sport Exercise Health . (2019) 11:589–97. doi: 10.1080/2159676X.2019.1628806

28. Delespaul P, Milo M, Schalken F, Boevink W, Van Os J. Goede GGZ!: Nieuwe concepten, aangepaste taal en betere organisatie . Houten: Springer (2017). doi: 10.1007/978-90-368-2062-2

29. Smit D, Van Os J. De praktijk binnen de ggz is in ontwikkeling, volgt de wetenschap? Tijdschrift voor Psychiatr . (2022) 10:692–5.

30. Van Os J. Vertaalt de sterke sociale gradiënt van de ggz zich in een uniform sterke samenhang tussen ggz en sociale zorg? Tijdschrift voor Psychiatr . (2022) 64:366–76.

31. Bosch J. Koerswijziging in de geestelijke gezondheidszorg. Tijdschrift voor Gedragstherapie . (2023) 2023:91–4.

32. Williams J, Bretteville-Jensen AL. What's another day? The effects of wait time for substance abuse treatment on health-care utilization, employment and crime. IZA Discussion Paper No 15083 . (2022) 15083. doi: 10.2139/ssrn.4114629

33. Haggerty JL, Reid RJ, Freeman GK, Starfield BH, Adair CE, McKendry R. Continuity of care: A multidisciplinary review. BMJ . (2003) 327:1219–21. doi: 10.1136/bmj.327.7425.1219

34. Burns T, Fioritti A, Holloway F, Malm U, Rössler W. Case management and assertive community treatment in Europe. Psychiatr Services . (2001) 52:631–6. doi: 10.1176/appi.ps.52.5.631

35. Dieterich M, Irving CB, Bergman H, Khokhar MA, Park B, Marshall M. Intensive case management for severe mental illness. Cochrane Database Systematic Rev . (2017) 1. doi: 10.1002/14651858

36. Trane K, Aasbrenn K, Rønningen M, Odden S, Lexén A, Landheim A. Flexible assertive community treatment teams can change complex and fragmented service systems: Experiences of service providers. Int J Ment Health Systems . (2021) 15:1–12. doi: 10.1186/s13033-021-00463-1

37. O’Connor K, Neff DM, Pitman S. Burnout in mental health professionals: A systematic review and meta-analysis of prevalence and determinants. Eur Psychiatry . (2018) 53:74–99. doi: 10.1016/j.eurpsy.2018.06.003

38. Vanderplasschen W, Rapp RC, De Maeyer J, Van Den Noortgate W. A meta-analysis of the efficacy of case management for substance use disorders: A recovery perspective. Front Psychiatry . (2019) 10:186. doi: 10.3389/fpsyt.2019.00186

39. Burns T, Catty J, White S, Clement S, Ellis G, Jones IR, et al. Continuity of care in mental health: understanding and measuring a complex phenomenon. psychol Med . (2009) 39:313–23. doi: 10.1017/S0033291708003747

40. Burch PB. Beyond relational continuity. Br J Gen Practice . (2021) 71:347. doi: 10.3399/bjgp21X716537

41. Nugter MA, Engelsbel F, Bähler M, Keet R, van Veldhuizen R. Outcomes of FLEXIBLE assertive community treatment (FACT) implementation: A prospective real life study. Community Ment Health J . (2016) 52:898–907. doi: 10.1007/s10597-015-9831-2

42. Earnshaw VA. Stigma and substance use disorders: A clinical, research, and advocacy agenda. Am Psychol . (2020) 75:1300–11. doi: 10.1037/amp0000744

43. Wogen J, Restrepo MT. Human rights, stigma, and substance use. Health Hum Rights . (2020) 22:51–60.

PubMed Abstract | Google Scholar

44. Atayde AM, Hauc SC, Bessette LG, Danckers H, Saitz R. Changing the narrative: A call to end stigmatizing terminology related to substance use disorders. Addict Res Theory . (2021) 29:359–62. doi: 10.1080/16066359.2021.1875215

45. Tomlinson P. De Saga Van Mijn Diagnoses: een autobiografisch narratief. Tijdschrift Verslaving Herstel . (2021) 1:10–4.

46. Kessing ML. Doing peer work in mental health services: Unpacking different enactments of lived experiences. Health Sociology Review . (2022) 31:32–46. doi: 10.1080/14461242.2020.1865183

47. Eddie D, Hoffman L, Vilsaint C, Abry A, Bergman B, Hoeppner B, et al. Lived experience in new models of care for substance use disorder: a systematic review of peer recovery support services and recovery coaching. Front Psychol . (2019) 10:1052. doi: 10.3389/fpsyg.2019.01052

48. Åkerblom KB, Ness O. Peer workers in co-production and co-creation in mental health and substance use services: a scoping review. Administration Policy Ment Health Ment Health Serv Res . (2023) 50:296–316. doi: 10.1007/s10488-022-01242-x

49. Davidson L, Andres-Hyman R, Bedregal L, Tondora J, Frey J, Kirk TA Jr. From “double trouble” to “dual recovery”: Integrating models of recovery in addiction and mental health. J Dual Diagnosis . (2008) 4:273–90. doi: 10.1080/15504260802072396

50. Palmieri CD, Accordino MP. Dual diagnosis: Effective treatment and barriers to recovery. J Appl Rehabil Counseling . (2004) 35:35–41. doi: 10.1891/0047-2220.35.4.35

51. Giordano AL, Prosek EA, Stamman J, Callahan MM, Loseu S, Bevly CM, et al. Addressing trauma in substance abuse treatment. J Alcohol Drug Education . (2016) 60:55–71.

52. De Ruysscher C. Unraveling recovery: a qualitative study rooted in the lived experiences of persons with complex mental health needs . Ghent (Belgium): Ghent University (2019).

53. Ford JH 2nd, Rao D, Gilson A, Kaur A, Chokron Garneau H, Saldana L, et al. Wait no longer: Reducing medication wait-times for individuals with co-occurring disorders. J Dual Diagnosis . (2022) 18:101–10. doi: 10.1080/15504263.2022.2052225

54. Ness O, Borg M, Davidson L. Facilitators and barriers in dual recovery: A literature review of first-person perspectives. Adv Dual Diagnosis . (2014) 7:107. doi: 10.1108/ADD-02-2014-0007

55. Pearce EJ. No wrong door: The limitations of intervention models for substance misuse individuals who have co-existing mental health problems . Sydney (Australia): UNSW Sydney (2010).

56. Xin Y, España M, Davis AK. The acceptability of non-abstinent treatment goals among clinical social workers in the United States. J Soc Work Practice . (2023) 37:339–54. doi: 10.1080/02650533.2022.2034768

57. Bell J, Lim A, Williams R, Girdler S, Milbourn B, Black M. ‘Nothing about us without us': co-production ingredients for working alongside stakeholders to develop mental health interventions. Adv Ment Health . (2023) 21:4–16. doi: 10.1080/18387357.2021.2020143

Keywords: mental health care, substance use disorders, recovery, treatment services, accessibility

Citation: De Ruysscher C, Magerman J, Goethals I, Chantry M, Sinclair DL, Delespaul P, De Maeyer J, Nicaise P and Vanderplasschen W (2024) Islands in the stream: a qualitative study on the accessibility of mental health care for persons with substance use disorders in Belgium. Front. Psychiatry 15:1344020. doi: 10.3389/fpsyt.2024.1344020

Received: 24 November 2023; Accepted: 01 July 2024; Published: 12 July 2024.

Reviewed by:

Copyright © 2024 De Ruysscher, Magerman, Goethals, Chantry, Sinclair, Delespaul, De Maeyer, Nicaise and Vanderplasschen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Clara De Ruysscher, [email protected]

†These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

A worked example of Braun and Clarke’s approach to reflexive thematic analysis

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Since the publication of their inaugural paper on the topic in 2006, Braun and Clarke’s approach has arguably become one of the most thoroughly delineated methods of conducting thematic analysis (TA). However, confusion persists as to how to implement this specific approach to TA appropriately. The authors themselves have identified that many researchers who purport to adhere to this approach—and who reference their work as such—fail to adhere fully to the principles of ‘reflexive thematic analysis’ (RTA). Over the course of numerous publications, Braun and Clarke have elaborated significantly upon the constitution of RTA and attempted to clarify numerous misconceptions that they have found in the literature. This paper will offer a worked example of Braun and Clarke’s contemporary approach to reflexive thematic analysis with the aim of helping to dispel some of the confusion regarding the position of RTA among the numerous existing typologies of TA. While the data used in the worked example has been garnered from health and wellbeing education research and was examined to ascertain educators’ attitudes regarding such, the example offered of how to implement the RTA would be easily transferable to many other contexts and research topics.

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1 Introduction

Although the lineage of thematic analysis (TA) can be traced back as far as the early twentieth century (Joffe 2012 ), it has up until recently been a relatively poorly demarcated and poorly understood method of qualitative analysis. Much of the credit for the recent enlightenment and subsequent increase in interest in TA can arguably be afforded to Braun and Clarke’s ( 2006 ) inaugural publication on the topic of thematic analysis in the field of psychology. These authors have since published several articles and book chapters, as well as their own book, all of which make considerable contributions to further delineating their approach to TA (see, for example, Braun and Clarke 2012 , 2013 , 2014 , 2019 , 2020 ; Braun et al. 2016 ; Terry et al. 2017 ). However, on numerous occasions Braun and Clarke have identified a tendency for scholars to cite their 2006 article, but fail to fully adhere to their contemporary approach to RTA (see Braun and Clarke 2013 , 2019 , 2020 ). Commendably, they have acknowledged that their 2006 paper left several aspect of their approach incompletely defined and open to interpretation. Indeed, the term ‘reflexive thematic analysis’ only recently came about in response to these misconceptions (Braun and Clarke 2019 ). Much of their subsequent body of literature in this area addresses these issues and attempts to correct some of the misconceptions in the wider literature regarding their approach. Braun and Clarke have repeatedly iterated that researchers who chose to adopt their approach should interrogate their relevant publications beyond their 2006 article and adhere to their contemporary approach (Braun and Clarke 2019 , 2020 ). The purpose of this paper is to contribute to dispelling some of the confusion and misconceptions regarding Braun and Clarke’s approach by providing a worked example of their contemporary approach to reflexive thematic analysis. The worked example will be presented in relation to the author’s own research, which examined the attitudes of post-primary educators’ regarding the promotion of student wellbeing. This paper is intended to be a supplementary resource for any prospective proponents of RTA, but may be of particular interest to scholars conducting attitudinal studies in an educational context. While this paper is aimed at all scholars regardless of research experience, it may be most useful to research students and their supervisors. Ultimately, the provided example of how to implement the six-phase analysis is easily transferable to many contexts and research topics.

2 What is reflexive thematic analysis?

Reflexive thematic analysis is an easily accessible and theoretically flexible interpretative approach to qualitative data analysis that facilitates the identification and analysis of patterns or themes in a given data set (Braun and Clarke 2012 ). RTA sits among a number of varied approaches to conducting thematic analysis. Braun and Clarke have noted that very often, researchers who purport to have adopted RTA have failed to fully delineate their implementation of RTA, of have confused RTA with other approaches to thematic analysis. The over-riding tendency in this regard is for scholars to mislabel their analysis as RTA, or to draw from a number of different approaches to TA, some of which may not be compatible with each other (Braun and Clarke 2012 , 2013 , 2019 ; Terry et al. 2017 ). In an attempt to resolve this confusion, Braun and Clarke have demarcated the position of RTA among the other forms of thematic analysis by differentiating between three principal approaches to TA: (1) coding reliability TA; (2) codebook approaches to TA, and; (3) the reflexive approach to TA (Braun et al. 2019 ).

Coding reliability approaches, such as those espoused by Boyatzis ( 1998 ) and Joffe ( 2012 ), accentuate the measurement of accuracy or reliability when coding data, often involving the use of a structured codebook. The researcher would also seek a degree of consensus among multiple coders, which can be measured using Cohen’s Kappa (Braun and Clarke 2013 ). When adopting a coding reliability approach, themes tend to be developed very early in the analytical process. Themes can be hypothesised based on theory prior to data collection, with evidence to support these hypotheses then gathered from the data in the form of codes. Alternatively, themes can be hypothesised following a degree of familiarisation with the data (Terry et al. 2017 ). Themes are typically understood to constitute ‘domain summaries’, or “summaries of what participants said in relation to a particular topic or data collection question” (Braun et al. 2019 , p. 5), and are likely to be discussed as residing within the data in a positivistic sense.

Codebook approaches, such as framework analysis (Smith and Firth 2011 ) or template analysis (King and Brooks 2017 ), can be understood to be something of a mid-point between coding reliability approaches and the reflexive approach. Like coding reliability approaches, codebook approaches adopt the use of a structured codebook and share the conceptualisation of themes as domain summaries. However, codebook approaches are more akin to the reflexive approach in terms of the prioritisation of a qualitative philosophy with regard to coding. Proponents of codebook approaches would typically forgo positivistic conceptions of coding reliability, instead recognising the interpretive nature of data coding (Braun et al. 2019 ).

The reflexive approach to TA highlights the researcher’s active role in knowledge production (Braun and Clarke 2019 ). Codes are understood to represent the researcher’s interpretations of patterns of meaning across the dataset. Reflexive thematic analysis is considered a reflection of the researcher’s interpretive analysis of the data conducted at the intersection of: (1) the dataset; (2) the theoretical assumptions of the analysis, and; (3) the analytical skills/resources of the researcher (Braun and Clarke 2019 ). It is fully appreciated—even expected—that no two researchers will intersect this tripartite of criteria in the same way. As such, there should be no expectation that codes or themes interpreted by one researcher may be reproduced by another (although, this is of course possible). Prospective proponents of RTA are discouraged from attempting to provide accounts of ‘accurate’ or ‘reliable’ coding, or pursuing consensus among multiple coders or using Cohen’s Kappa values. Rather, RTA is about “the researcher’s reflective and thoughtful engagement with their data and their reflexive and thoughtful engagement with the analytic process” (Braun and Clarke 2019 , p. 594). Multiple coders may, however, be beneficial in a reflexive manner (e.g. to sense-check ideas, or to explore multiple assumptions or interpretations of the data). If analysis does involve more than one researcher, the approach should be collaborative and reflexive, aiming to achieve richer interpretations of meaning, rather than attempting to achieve consensus of meaning. Indeed, in this sense it would be beneficial for proponents of RTA to remain cognisant that qualitative analysis as a whole does not contend to provide a single or ‘correct’ answer (Braun and Clarke 2013 ).

The process of coding (and theme development) is flexible and organic, and very often will evolve throughout the analytical process (Braun et al. 2019 ). Progression through the analysis will tend to facilitate further familiarity with the data, which may in turn result in the interpretation of new patterns of meaning. This is converse to the use of codebooks, which can often predefine themes before coding. Through the reflexive approach, themes are not predefined in order to ‘find’ codes. Rather, themes are produced by organising codes around a relative core commonality, or ‘central organising concept’, that the researcher interprets from the data (Braun and Clarke 2019 ).

In their 2006 paper, Braun and Clarke ( 2006 ) originally conceptualised RTA as a paradigmatically flexible analytical method, suitable for use within a wide range of ontological and epistemological considerations. In recent publications, the authors have moved away from this view, instead defining RTA as a purely qualitative approach. This pushes the use RTA into exclusivity under appropriate qualitative paradigms (e.g. constructionism) (Braun and Clarke 2019 , 2020 ). As opposed to other forms of qualitative analysis such as content analysis (Vaismoradi et al. 2013 ), and even other forms of TA such as Boyatzis’ ( 1998 ) approach, RTA eschews any positivistic notions of data interpretation. Braun and Clarke ( 2019 ) encourage the researcher to embrace reflexivity, subjectivity and creativity as assets in knowledge production, where they argue some scholars, such as Boyatzis ( 1998 ), may otherwise construe these assets as threats.

3 A worked example of reflexive thematic analysis

The data used in the following example is taken from the qualitative phase of a mixed methods study I conducted, which examined mental health in an educational context. This study set out to understand the attitudes and opinions of Irish post-primary educators with regard to the promotion of students’ social and emotional wellbeing, with the intention to feed this information back to key governmental and non-governmental stakeholders such as the National Council for Curriculum and Assessment and the Department of Education. The research questions for this study aimed to examine educators’ general attitudes toward the promotion of student wellbeing and towards a set of ‘wellbeing guidelines’ that had recently been introduced in Irish post-primary schools. I also wanted to identify any potential barriers to wellbeing promotion and to solicit educators’ opinions as to what might constitute apposite remedial measures in this regard.

The qualitative phase of this study, from which the data for this example is garnered, involved eleven semi-structured interviews, which lasted approximately 25–30 min each. Participants consisted of core-curriculum teachers, wellbeing curriculum teachers, pastoral care team-members and senior management members. Participants were questioned on their attitudes regarding the promotion of student wellbeing, the wellbeing curriculum, the wellbeing guidelines and their perceptions of their own wellbeing. When conducting these interviews, I loosely adhered to an interview agenda to ensure each of these four key topics were addressed. However, discussions were typically guided by what I interpreted to be meaningful to the interviewee, and would often weave in and out of these different topics.

The research questions for this study were addressed within a paradigmatic framework of interpretivism and constructivism. A key principle I adopted for this study was to reflect educators’ own accounts of their attitudes, opinions and experiences as faithfully as was possible, while also accounting for the reflexive influence of my own interpretations as the researcher. I felt RTA was highly appropriate in the context of the underlying theoretical and paradigmatic assumptions of my study and would allow me to ensure qualitative data was collected and analysed in a manner that respected and expressed the subjectivity of participants’ accounts of their attitudes, while also acknowledging and embracing the reflexive influence of my interpretations as the researcher.

In the next section, I will outline the theoretical assumptions of the RTA conducted in my original study in more detail. It should be noted that outlining these theoretical assumptions is not a task specific to reflexive thematic analysis. Rather, these assumptions should be addressed prior to implementing any form of thematic analysis (Braun and Clarke 2012 , 2019 , 2020 ; Braun et al. 2016 ). The six-phase process for conducting reflexive thematic analysis will then be appropriately detailed and punctuated with examples from my study.

3.1 Addressing underlying theoretical assumptions

Across several publications, Braun and Clarke ( 2012 , 2014 , 2020 ) have identified a number of theoretical assumptions that should be addressed when conducting RTA, or indeed any form of thematic analysis. These assumptions are conceptualised as a series of continua as follows: essentialist versus constructionist epistemologies; experiential versus critical orientation to data; inductive versus deductive analyses, and; semantic versus latent coding of data. The aim is not just for the researcher to identify where their analysis is situated on each of these continua, but why the analysis is situated as it is and why this conceptualisation is appropriate to answering the research question(s).

3.1.1 Essentialist versus constructionist epistemologies

Ontological and epistemological considerations would usually be determined when a study is first being conceptualised. However, these considerations may become salient again when data analysis becomes the research focus, particularly with regard to mixed methods. The purpose of addressing this continuum is to conceptualise theoretically how the researcher understands their data and the way in which the reader should interpret the findings (Braun and Clarke 2013 , 2014 ). By adhering to essentialism, the researcher adopts a unidirectional understanding of the relationship between language and communicated experience, in that it is assumed that language is a simple reflection of our articulated meanings and experiences (Widdicombe and Wooffiitt 1995 ). The meanings and systems inherent in constructing these meanings are largely uninterrogated, with the interpretive potential of TA largely unutilised (Braun et al. 2016 ).

Conversely, researchers of a constructionist persuasion would tend to adopt a bidirectional understanding of the language/experience relationship, viewing language as implicit in the social production and reproduction of both meaning and experience (Burr 1995 ; Schwandt 1998 ). A constructionist epistemology has particular implications with regard to thematic analysis, namely that in addition to the recurrence of perceptibly important information, meaningfulness is highly influential in the development and interpretation of codes and themes. The criteria for a theme to be considered noteworthy via recurrence is simply that the theme should present repeatedly within the data. However, what is common is not necessarily meaningful or important to the analysis. Braun and Clarke ( 2012 , p. 37) offer this example:

…in researching white-collar workers’ experiences of sociality at work, a researcher might interview people about their work environment and start with questions about their typical workday. If most or all reported that they started work at around 9:00 a.m., this would be a pattern in the data, but it would not necessarily be a meaningful or important one.

Furthermore, there may be varying degrees of conviction in respondents’ expression when addressing different issues that may facilitate in identifying the salience of a prospective theme. Therefore, meaningfulness can be conceptualised, firstly on the part of the researcher, with regard to the necessity to identify themes that are relevant to answering the research questions, and secondly on the part of the respondent, as the expression of varying degrees of importance with regard to the issues being addressed. By adopting a constructionist epistemology, the researcher acknowledges the importance of recurrence, but appreciates meaning and meaningfulness as the central criteria in the coding process.

In keeping with the qualitative philosophy of RTA, epistemological consideration regarding the example data were constructionist. As such, meaning and experience was interpreted to be socially produced and reproduced via an interplay of subjective and inter-subjective construction. Footnote 1

3.1.2 Experiential versus critical orientation

An experiential orientation to understanding data typically prioritises the examination of how a given phenomenon may be experienced by the participant. This involves investigating the meaning ascribed to the phenomenon by the respondent, as well as the meaningfulness of the phenomenon to the respondent. However, although these thoughts, feelings and experiences are subjectively and inter-subjectively (re)produced, the researcher would cede to the meaning and meaningfulness ascribed by the participant (Braun and Clarke 2014 ). Adopting an experiential orientation requires an appreciation that the thoughts, feelings and experiences of participants are a reflection of personal states held internally by the participant. Conversely, a critical orientation appreciates and analyses discourse as if it were constitutive, rather than reflective, of respondents’ personal states (Braun and Clarke 2014 ). As such, a critical perspective seeks to interrogate patterns and themes of meaning with a theoretical understanding that language can create, rather than merely reflect, a given social reality (Terry et al. 2017 ). A critical perspective can examine the mechanisms that inform the construction of systems of meaning, and therefore offer interpretations of meaning further to those explicitly communicated by participants. It is then also possible to examine how the wider social context may facilitate or impugn these systems of meaning (Braun and Clarke 2012 ). In short, the researcher uses this continuum to clarify their intention to reflect the experience of a social reality (experiential orientation) or examine the constitution of a social reality (critical orientation).

In the present example, an experiential orientation to data interpretation was adopted in order to emphasise meaning and meaningfulness as ascribed by participants. Adopting this approach meant that this analysis did not seek to make claims about the social construction of the research topic (which would more so necessitate a critical perspective), but rather acknowledged the socially constructed nature of the research topic when examining the subjective ‘personal states’ of participants. An experiential orientation was most appropriate as the aim of the study was to prioritise educators’ own accounts of their attitudes, opinions. More importantly, the research questions aimed to examine educators’ attitudes regarding their experience of promoting student wellbeing—or the ‘meanings made’—and not, for example, the socio-cultural factors that may underlie the development of these attitudes—or the ‘meaning making’.

3.1.3 Inductive versus deductive analysis

A researcher who adopts a deductive or ‘theory-driven’ approach may wish to produce codes relative to a pre-specified conceptual framework or codebook. In this case, the analysis would tend to be ‘analyst-driven’, predicated on the theoretically informed interpretation of the researcher. Conversely, a researcher who adopts an inductive or ‘data-driven’ approach may wish to produce codes that are solely reflective of the content of the data, free from any pre-conceived theory or conceptual framework. In this case, data are not coded to fit a pre-existing coding frame, but instead ‘open-coded’ in order to best represent meaning as communicated by the participants (Braun and Clarke 2013 ). Data analysed and coded deductively can often provide a less rich description of the overall dataset, instead focusing on providing a detailed analysis of a particular aspect of the dataset interpreted through a particular theoretical lens (Braun and Clarke 2020 ). Deductive analysis has typically been associated with positivistic/essentialist approaches (e.g. Boyatzis 1998 ), while inductive analysis tends to be aligned with constructivist approaches (e.g. Frith and Gleeson 2004 ). That being said, inductive/deductive approaches to analysis are by no means exclusively or intrinsically linked to a particular epistemology.

Coding and analysis rarely fall cleanly into one of these approaches and, more often than not, use a combination of both (Braun and Clarke 2013 , 2019 , 2020 ). It is arguably not possible to conduct an exclusively deductive analysis, as an appreciation for the relationship between different items of information in the data set is necessary in order to identify recurring commonalities with regard to a pre-specified theory or conceptual framework. Equally, it is arguably not possible to conduct an exclusively inductive analysis, as the researcher would require some form of criteria to identify whether or not a piece of information may be conducive to addressing the research question(s), and therefore worth coding. When addressing this issue, Braun and Clarke ( 2012 ) clarify that one approach does tend to predominate over the other, and that the predominance of the deductive or inductive approach can indicate an overall orientation towards prioritising either researcher/theory-based meaning or respondent/data-based meaning, respectively.

A predominantly inductive approach was adopted in this example, meaning data was open-coded and respondent/data-based meanings were emphasised. A degree of deductive analysis was, however, employed to ensure that the open-coding contributed to producing themes that were meaningful to the research questions, and to ensure that the respondent/data-based meanings that were emphasised were relevant to the research questions.

3.1.4 Semantic versus latent coding

Semantic codes are identified through the explicit or surface meanings of the data. The researcher does not examine beyond what a respondent has said or written. The production of semantic codes can be described as a descriptive analysis of the data, aimed solely at presenting the content of the data as communicated by the respondent. Latent coding goes beyond the descriptive level of the data and attempts to identify hidden meanings or underlying assumptions, ideas, or ideologies that may shape or inform the descriptive or semantic content of the data. When coding is latent, the analysis becomes much more interpretive, requiring a more creative and active role on the part of the researcher. Indeed, Braun and Clarke ( 2012 , 2013 , 2020 ) have repeatedly presented the argument that codes and themes do not ‘emerge’ from the data or that they may be residing in the data, waiting to be found. Rather, the researcher plays an active role in interpreting codes and themes, and identifying which are relevant to the research question(s). Analyses that use latent coding can often overlap with aspects of thematic discourse analysis in that the language used by the respondent can be used to interpret deeper levels of meaning and meaningfulness (Braun and Clarke 2006 ).

In this example, both semantic and latent coding were utilised. No attempt was made to prioritise semantic coding over latent coding or vice-versa. Rather, semantic codes were produced when meaningful semantic information was interpreted, and latent codes were produced when meaningful latent information was interpreted. As such, any item of information could be double-coded in accordance with the semantic meaning communicated by the respondent, and the latent meaning interpreted by the researcher (Patton 1990 ). This was reflective of the underlying theoretical assumptions of the analysis, as the constructive and interpretive epistemology and ontology were addressed by affording due consideration to both the meaning constructed and communicated by the participant and my interpretation of this meaning as the researcher.

3.2 The six-phase analytical process

Braun and Clarke ( 2012 , 2013 , 2014 , 2020 ) have proposed a six-phase process, which can facilitate the analysis and help the researcher identify and attend to the important aspects of a thematic analysis. In this sense, Braun and Clarke ( 2012 ) have identified the six-phase process as an approach to doing TA, as well as learning how to do TA. While the six phases are organised in a logical sequential order, the researcher should be cognisant that the analysis is not a linear process of moving forward through the phases. Rather, the analysis is recursive and iterative, requiring the researcher to move back and forth through the phases as necessary (Braun and Clarke 2020 ). TA is a time consuming process that evolves as the researcher navigates the different phases. This can lead to new interpretations of the data, which may in turn require further iterations of earlier phases. As such, it is important to appreciate the six-phase process as a set of guidelines, rather than rules, that should be applied in a flexible manner to fit the data and the research question(s) (Braun and Clarke 2013 , 2020 ).

3.2.1 Phase one: familiarisation with the data

The ‘familiarisation’ phase is prevalent in many forms of qualitative analysis. Familiarisation entails the reading and re-reading of the entire dataset in order to become intimately familiar with the data. This is necessary to be able to identify appropriate information that may be relevant to the research question(s). Manual transcription of data can be a very useful activity for the researcher in this regard, and can greatly facilitate a deep immersion into the data. Data should be transcribed orthographically, noting inflections, breaks, pauses, tones, etc. on the part of both the interviewer and the participant (Braun and Clarke 2013 ). Often times, data may not have been gathered or transcribed by the researcher, in which case, it would be beneficial for the researcher to watch/listen to video or audio recordings to achieve a greater contextual understanding of the data. This phase can be quite time consuming and requires a degree of patience. However, it is important to afford equal consideration across the entire depth and breadth of the dataset, and to avoid the temptation of being selective of what to read, or even ‘skipping over’ this phase completely (Braun and Clarke 2006 ).

At this phase, I set about familiarising myself with the data by firstly listening to each interview recording once before transcribing that particular recording. This first playback of each interview recording required ‘active listening’ and, as such, I did not take any notes at this point. I performed this active-listen in order to develop an understanding of the primary areas addressed in each interview prior to transcription. This also provided me an opportunity, unburdened by tasks such as note taking, to recall gestures and mannerisms that may or may not have been documented in interview notes. I manually transcribed each interview immediately after the active-listen playback. When transcription of all interviews was complete, I read each transcripts numerous times. At this point, I took note of casual observations of initial trends in the data and potentially interesting passages in the transcripts. I also documented my thoughts and feelings regarding both the data and the analytical process (in terms of transparency, it would be beneficial to adhere to this practice throughout the entire analysis). Some preliminary notes made during the early iterations of familiarisation with the data can be seen in Box 1. It will be seen later that some of these notes would go on to inform the interpretation of the finalised thematic framework.

Example of preliminary notes taken during phase one

3.2.2 Phase two: generating initial codes

Codes are the fundamental building blocks of what will later become themes. The process of coding is undertaken to produce succinct, shorthand descriptive or interpretive labels for pieces of information that may be of relevance to the research question(s). It is recommended that the researcher work systematically through the entire dataset, attending to each data item with equal consideration, and identifying aspects of data items that are interesting and may be informative in developing themes. Codes should be brief, but offer sufficient detail to be able to stand alone and inform of the underlying commonality among constituent data items in relation to the subject of the research (Braun and Clarke 2012 ; Braun et al. 2016 ).

A brief excerpt of the preliminary coding process of one participant’s interview transcript is presented in Box 2. The preliminary iteration of coding was conducted using the ‘comments’ function in Microsoft Word (2016). This allowed codes to be noted in the side margin, while also highlighting the area of text assigned to each respective code. This is a relatively straightforward example with no double-codes or overlap in data informing different codes, as new codes begin where previous codes end. The code C5 offers an exemplar of the provision of sufficient detail to explain what I interpreted from the related data item. A poor example of this code would be to say “the wellbeing guidelines are not relatable” or “not relatable for students”. Each of these examples lack context. Understanding codes written in this way would be contingent upon knowledge of the underlying data extract. The code C8 exemplifies this issue. It is unclear if the positivity mentioned relates to the particular participant, their colleagues, or their students. This code was subsequently redefined in later iterations of coding. It can also be seen in this short example that the same code has been produced for both C4 and C9. This code was prevalent throughout the entire dataset and would subsequently be informative in the development of a theme.

Extract of preliminary coding

Any item of data that might be useful in addressing the research question(s) should be coded. Through repeated iterations of coding and further familiarisation, the researcher can identify which codes are conducive to interpreting themes and which can be discarded. I would recommend that the researcher document their progression through iterations of coding to track the evolution of codes and indeed prospective themes. RTA is a recursive process and it is rare that a researcher would follow a linear path through the six phases (Braun and Clarke 2014 ). It is very common for the researcher to follow a particular train of thought when coding, only to encounter an impasse where several different interpretations of the data come to light. It may be necessary to explore each of these prospective options to identify the most appropriate path to follow. Tracking the evolution of codes will not only aid transparency, but will afford the researcher signposts and waypoints to which they may return should a particular approach to coding prove unfruitful. I tracked the evolution of my coding process in a spreadsheet, with data items documented in the first column and iterations of codes in each successive column. I found it useful to highlight which codes were changed in each successive iteration. Table 1 provides an excerpt of a Microsoft Excel (2016) spreadsheet that was established to track iterations of coding and document the overall analytical process. All codes developed during the first iteration of coding were transferred into this spreadsheet along with a label identifying the respective participant. Subsequent iterations of coding were documented in this spreadsheet. The original transcripts were still regularly consulted to assess existing codes and examine for the interpretation of new codes as further familiarity with the data developed. Column one presents a reference number for the data item that was coded, while column two indicates the participant who provided each data item. Column three presents the data item that was coded. Columns four and five indicate the iteration of the coding process to be the third and fourth iteration, respectively. Codes revised between iterations three and four are highlighted.

With regard to data item one, I initially considered that a narrative might develop exploring a potential discrepancy in levels of training received by wellbeing educators and non-wellbeing educators. In early iterations of coding, I adopted a convention of coding training-related information with reference to the wellbeing or non-wellbeing status of the participant. While this discrepancy in levels of training remained evident throughout the dataset, I eventually deemed it unnecessary to pursue interpretation of the data in this way. This coding convention was abandoned at iteration four in favour of the pre-existing generalised code “insufficient training in wellbeing curriculum”. With data item three, I realised that the code was descriptive at a semantic level, but not very informative. Upon re-evaluating this data item, I found the pre-existing code “lack of clarity in assessing student wellbeing” to be much more appropriate and representative of what the participant seemed to be communicating. Finally, I realised that the code for data item five was too specific to this particular data item. No other data item shared this code, which would preclude this code (and data item) from consideration when construction themes. I decided that this item would be subsumed under the pre-existing code “more training is needed for wellbeing promotion”.

The process of generating codes is non-prescriptive regarding how data is segmented and itemised for coding, and how many codes or what type of codes (semantic or latent) are interpreted from an item of data. The same data item can be coded both semantically and latently if deemed necessary. For example, when discussing how able they felt to attend to their students’ wellbeing needs, one participant stated “…if someone’s struggling a bit with their schoolwork and it’s getting them down a bit, it’s common sense that determines what we say to them or how we approach them. And it might help to talk, but I don’t know that it has a lasting effect” [2B]. Here, I understood that the participant was explicitly sharing the way in which they address their students’ wellbeing concerns, but also that the participant was implying that this commonsense approach might not be sufficient. As such, this data item was coded both semantically as “educators rely on common sense when attending to wellbeing issues”, and latently as “common sense inadequate for wellbeing promotion”. Both codes were revised later in the analysis. However, this example illustrates the way in which any data item can be coded in multiple ways and for multiple meanings. There is also no upper or lower limit regarding how many codes should be interpreted. What is important is that, when the dataset is fully coded and codes are collated, sufficient depth exists to examine the patterns within the data and the diversity of the positions held by participants. It is, however, necessary to ensure that codes pertain to more than one data item (Braun and Clarke 2012 ).

3.2.3 Phase three: generating themes

This phase begins when all relevant data items have been coded. The focus shifts from the interpretation of individual data items within the dataset, to the interpretation of aggregated meaning and meaningfulness across the dataset. The coded data is reviewed and analysed as to how different codes may be combined according to shared meanings so that they may form themes or sub-themes. This will often involve collapsing multiple codes that share a similar underlying concept or feature of the data into one single code. Equally, one particular code may turn out to be representative of an over-arching narrative within the data and be promoted as a sub-theme or even a theme (Braun and Clarke 2012 ). It is important to re-emphasise that themes do not reside in the data waiting to be found. Rather, the researcher must actively construe the relationship among the different codes and examine how this relationship may inform the narrative of a given theme. Construing the importance or salience of a theme is not contingent upon the number of codes or data items that inform a particular theme. What is important is that the pattern of codes and data items communicates something meaningful that helps answer the research question(s) (Braun and Clarke 2013 ).

Themes should be distinctive and may even be contradictory to other themes, but should tie together to produce a coherent and lucid picture of the dataset. The researcher must be able and willing to let go of codes or prospective themes that may not fit within the overall analysis. It may be beneficial to construct a miscellaneous theme (or category) to contain all the codes that do not appear to fit in among any prospective themes. This miscellaneous theme may end up becoming a theme in its own right, or may simple be removed from the analysis during a later phase (Braun and Clarke 2012 ). Much the same as with codes, there is no correct amount of themes. However, with too many themes the analysis may become unwieldy and incoherent, whereas too few themes can result in the analysis failing to explore fully the depth and breadth of the data. At the end of this stage, the researcher should be able to produce a thematic map (e.g. a mind map or affinity map) or table that collates codes and data items relative to their respective themes (Braun and Clarke 2012 , 2020 ).

At this point in the analysis, I assembled codes into initial candidate themes. A thematic map of the initial candidate themes can be seen in Fig.  1 . The theme “best practice in wellbeing promotion” was clearly definable, with constituent coded data presenting two concurrent narratives. These narratives were constructed as two separate sub-themes, which emphasised the involvement of the entire school staff and the active pursuit of practical measures in promoting student wellbeing, respectively. The theme “recognising student wellbeing” was similarly clear. Again, I interpreted a dichotomy of narratives. However, in this case, the two narratives seemed to be even more synergetic. The two sub-themes for “best practice…” highlighted two independently informative factors in best practice. Here, the sub-themes are much more closely related, with one sub-theme identifying factors that may inhibit the development of student wellbeing, while the second sub-theme discusses factors that may improve student wellbeing. At this early stage in the analysis, I was considering that this sub-theme structure might also be used to delineate the theme “recognising educator wellbeing”. Finally, the theme “factors influencing wellbeing promotion” collated coded data items that addressed inhibitive factors with regard to wellbeing promotion. These factors were conceptualised as four separate sub-themes reflecting a lack of training, a lack of time, a lack of appropriate value for wellbeing promotion, and a lack of knowledge of supporting wellbeing-related documents. While it was useful to bring all of this information together under one theme, even at this early stage it was evident that this particular theme was very dense and unwieldy, and would likely require further revision.

Initial thematic map indicating four candidate themes

3.2.4 Phase four: reviewing potential themes

This phase requires the researcher to conduct a recursive review of the candidate themes in relation to the coded data items and the entire dataset (Braun and Clarke 2012 , 2020 ). At this phase, it is not uncommon to find that some candidate themes may not function well as meaningful interpretations of the data, or may not provide information that addresses the research question(s). It may also come to light that some of the constituent codes and/or data items that inform these themes may be incongruent and require revision. Braun and Clarke ( 2012 , p. 65) proposed a series of key questions that the researcher should address when reviewing potential themes. They are:

Is this a theme (it could be just a code)?

If it is a theme, what is the quality of this theme (does it tell me something useful about the data set and my research question)?

What are the boundaries of this theme (what does it include and exclude)?

Are there enough (meaningful) data to support this theme (is the theme thin or thick)?

Are the data too diverse and wide ranging (does the theme lack coherence)?

The analysis conducted at this phase involves two levels of review. Level one is a review of the relationships among the data items and codes that inform each theme and sub-theme. If the items/codes form a coherent pattern, it can be assumed that the candidate theme/sub-theme makes a logical argument and may contribute to the overall narrative of the data. At level two, the candidate themes are reviewed in relation to the data set. Themes are assessed as to how well they provide the most apt interpretation of the data in relation to the research question(s). Braun and Clarke have proposed that, when addressing these key questions, it may be useful to observe Patton’s ( 1990 ) ‘dual criteria for judging categories’ (i.e. internal homogeneity and external heterogeneity). The aim of Patton’s dual criteria would be to observe internal homogeneity within themes at the level one review, while observing external heterogeneity among themes at the level two review. Essentially, these two levels of review function to demonstrate that items and codes are appropriate to inform a theme, and that a theme is appropriate to inform the interpretation of the dataset (Braun and Clarke 2006 ). The outcome of this dual-level review is often that some sub-themes or themes may need to be restructured by adding or removing codes, or indeed adding or removing themes/sub-themes. The finalised thematic framework that resulted from the review of the candidate themes can be seen in Fig.  2 .

Finalised thematic map demonstrating five themes

During the level one review, inspection of the prospective sub-theme “sources of negative affect” in relation to the theme “recognising educator wellbeing” resulted in a new interpretation of the constituent coded data items. Participants communicated numerous pre-existing work-related factors that they felt had a negative impact upon their wellbeing. However, it was also evident that participants felt the introduction of the new wellbeing curriculum and the newly mandated task of formally attending to student wellbeing had compounded these pre-existing issues. While pre-existing issues and wellbeing-related issues were both informative of educators’ negative affect, the new interpretation of this data informed the realisation of two concurrent narratives, with wellbeing-related issues being a compounding factor in relation to pre-existing issues. This resulted in the “sources of negative affect” sub-theme being split into two new sub-themes; “work-related negative affect” and “the influence of wellbeing promotion”. The “actions to improve educator wellbeing” sub-theme was folded into these sub-themes, with remedial measures for each issue being discussed in respective sub-themes.

During the level two review, my concerns regarding the theme “factors inhibiting wellbeing promotion” were addressed. With regard to Braun and Clarke’s key questions, it was quite difficult to identify the boundaries of this theme. It was also particularly dense (or too thick) and somewhat incoherent. At this point, I concluded that this theme did not constitute an appropriate representation of the data. Earlier phases of the analysis were reiterated and new interpretations of the data were developed. This candidate theme was subsequently broken down into three separate themes. While the sub-themes of this candidate theme were, to a degree, informative in the development of the new themes, the way in which the constituent data was understood was fundamentally reconceptualised. The new theme, entitled “the influence of time”, moves past merely describing time constraints as an inhibitive factor in wellbeing promotion. A more thorough account of the bi-directional nature of time constraints was realised, which acknowledged that previously existing time constraints affected wellbeing promotion, while wellbeing promotion compounded previously existing time constraints. This added an analysis of the way in which the introduction of wellbeing promotion also produced time constraints in relation to core curricular activities.

The candidate sub-themes “lack of training” and “knowledge of necessary documents” were re-evaluated and considered to be topical rather than thematic aspects of the data. Upon further inspection, I felt that the constituent coded data items of these two sub-themes were informative of a single narrative of participants attending to their students’ wellbeing in an atheoretical manner. As such, these two candidate sub-themes were folded into each other to produce the theme “incompletely theorised agreements”. Finally, the level two review led me to the conclusion that the full potential of the data that informed the candidate sub-theme “lack of value of wellbeing promotion” was not realised. I found that a much richer understanding of this data was possible, which was obscured by the initial, relatively simplistic, descriptive account offered. An important distinction was made, in that participants held differing perceptions of the value attributed to wellbeing promotion by educators and by students. Further, I realised that educators’ perceptions of wellbeing promotion were not necessarily negative and should not be exclusively presented as an inhibitive factor in wellbeing promotion. A new theme, named “the axiology of wellbeing” and informed by the sub-themes “students’ valuation of wellbeing promotion” and “educators’ valuation of wellbeing promotion”, was developed to delineate this multifaceted understanding of participants’ accounts of the value of wellbeing promotion.

It is quite typical at this phase that codes, as well as themes, may be revised or removed to facilitate the most meaningful interpretation of the data. As such, it may be necessary to reiterate some of the activities undertaken during phases two and three of the analysis. It may be necessary to recode some data items, collapse some codes into one, remove some codes, or promote some codes as sub-themes or themes. For example, when re-examining the data items that informed the narrative of the value ascribed to wellbeing promotion, I observed that participants offered very different perceptions of the value ascribed by educators and by students. To pursue this line of analysis, numerous codes were reconceptualised to reflect the two different perspectives. Codes such as “positivity regarding the wellbeing curriculum” were split into the more specified codes “student positivity regarding the wellbeing curriculum” and “educator positivity regarding the wellbeing curriculum”. Amending codes in this way ultimately contributed to the reinterpretation of the data and the development of the finalised thematic map.

As with all other phases, it is very important to track and document all of these changes. With regard to some of the more significant changes (removing a theme, for example), I would recommend making notes on why it might be necessary to take this action. The aim of this phase is to produce a revised thematic map or table that captures the most important elements of the data in relation to the research question(s).

3.2.5 Phase five: defining and naming theme

At this phase, the researcher is tasked with presenting a detailed analysis of the thematic framework. Each individual theme and sub-theme is to be expressed in relation to both the dataset and the research question(s). As per Patton’s ( 1990 ) dual criteria, each theme should provide a coherent and internally consistent account of the data that cannot be told by the other themes. However, all themes should come together to create a lucid narrative that is consistent with the content of the dataset and informative in relation to the research question(s). The names of the themes are also subject to a final revision (if necessary) at this point.

Defining themes requires a deep analysis of the underlying data items. There will likely be many data items underlying each theme. It is at this point that the researcher is required to identify which data items to use as extracts when writing up the results of the analysis. The chosen extracts should provide a vivid and compelling account of the arguments being made by a respective theme. Multiple extracts should be used from the entire pool of data items that inform a theme in order to convey the diversity of expressions of meaning across these data items, and to demonstrate the cohesion of the theme’s constituent data items. Furthermore, each of the reported data extracts should be subject to a deep analysis, going beyond merely reporting what a participant may have said. Each extract should be interpreted in relation to its constitutive theme, as well as the broader context of the research question(s), creating an analytic narrative that informs the reader what is interesting about this extract and why (Braun and Clarke 2012 ).

Data extracts can be presented either illustratively, providing a surface-level description of what participants said, or analytically, interrogating what has been interpreted to be important about what participants said and contextualising this interpretation in relation to the available literature. If the researcher were aiming to produce a more illustrative write-up of the analysis, relating the results to the available literature would tend to be held until the ‘discussion’ section of the report. If the researcher were aiming to produce an analytical write-up, extracts would tend to be contextualised in relation to the literature as and when they are reported in the ‘results’ section (Braun and Clarke 2013 ; Terry et al. 2017 ). While an illustrative write-up of RTA results is completely acceptable, the researcher should remain cognisant that the narrative of the write-up should communicate the complexities of the data, while remaining “embedded in the scholarly field” (Braun and Clarke 2012 , p. 69). RTA is an interpretive approach to analysis and, as such, the overall report should go beyond describing the data, providing theoretically informed arguments as to how the data addresses the research question(s). To this end, a relatively straightforward test can reveal a researcher’s potential proclivity towards one particular reporting convention: If an extract can be removed and the write-up still makes sense, the reporting style is illustrative; if an extract is removed and the write-up no longer makes sense, the reporting style is analytical (Terry et al. 2017 ).

The example in Box 3 contains a brief excerpt from the sub-theme “the whole-school approach”, which demonstrates the way in which a data extract may be reported in an illustrative manner. Here, the narrative discussed the necessity of having an ‘appropriate educator’ deliver the different aspects of the wellbeing curriculum. One participant provided a particularly useful real-world example of the potential negative implications of having ‘the wrong person’ for this job in relation to physical education (one of the aspects of the wellbeing curriculum). This data extract very much informed the narrative and illustrated participants’ arguments regarding the importance of choosing an appropriate educator for the job.

Example of data extract reported illustratively

In Box 4, an example is offered of how a data extract may be reported in an analytical manner. This excerpt is also taken from the sub-theme “the whole-school approach”, and also informs the ‘appropriate educator for the job’ narrative. Here, however, sufficient evidence has already been established to illustrate the perspectives of the participants. The report turns to a deeper analysis of what has been said and how it has been said. Specifically, the way in which participants seemed to construe an ‘appropriate educator’ was examined and related to existing literature. The analytical interpretation of this data extract (and others) proposes interesting implications regarding the way in which participants constructed their schema of an ‘appropriate educator’.

Example of data extract reported analytically

The names of themes are also subject to a final review (if necessary) at this point. Naming themes may seem trivial and might subsequently receive less attention than it actually requires. However, naming themes is a very important task. Theme names are the first indication to the reader of what has been captured from the data. Names should be concise, informative, and memorable. The overriding tendency may be to create names that are descriptors of the theme. Braun and Clarke ( 2013 , 2014 , 2020 ) encourage creativity and advocate the use of catchy names that may more immediately capture the attention of the reader, while also communicating an important aspect of the theme. To this end, they suggest that it may be useful to examine data items for a short extract that could be used to punctuate the theme name.

3.2.6 Phase six: producing the report

The separation between phases five and six can often be blurry. Further, this ‘final’ phase would rarely only occur at the end of the analysis. As opposed to practices typical of quantitative research that would see the researcher conduct and then write up the analysis, the write-up of qualitative research is very much interwoven into the entire process of the analysis (Braun and Clarke 2012 ). Again, as with previous phases, this will likely require a recursive approach to report writing. As codes and themes change and evolve over the course of the analysis, so too can the write-up. Changes should be well documented by this phase and reflected in informal notes and memos, as well as a research journal that should be kept over the entire course of the research. Phase six then, can be seen as the completion and final inspection of the report that the researcher would most likely have begun writing before even undertaking their thematic analysis (e.g. a journal article or thesis/dissertation).

A useful task to address at this point would be to establish the order in which themes are reported. Themes should connect in a logical and meaningful manner, building a cogent narrative of the data. Where relevant, themes should build upon previously reported themes, while remaining internally consistent and capable of communicating their own individual narrative if isolated from other themes (Braun and Clarke 2012 ). I reported the theme “best practice in wellbeing promotion” first, as I felt it established the positivity that seemed to underlie the accounts provided by all of my participants. This theme was also strongly influence by semantic codes, with participants being very capable of describing what they felt would constitute ‘best practice’. I saw this as an easily digestible first theme to ease the reader into the wider analysis. It made sense to report “the axiology of wellbeing promotion” next. This theme introduced the reality that, despite an underlying degree of positivity, participants did indeed have numerous concerns regarding wellbeing promotion, and that participants’ attitudes were generally positive with a significant ‘but’. This theme provided good sign-posting for the next two themes that would be reported, which were “the influence of time” and “incompletely theorised agreements”, respectively. I reported “the influence of time” first, as this theme established how time constraints could negatively affect educator training, contributing to a context in which educators were inadvertently pushed towards adopting incompletely theorised agreements when promoting student wellbeing. The last theme to be reported was “recognising educator wellbeing”. As the purpose of the analysis was to ascertain the attitudes of educators regarding wellbeing promotion, it felt appropriate to offer the closing commentary of the analysis to educators’ accounts of their own wellbeing. This became particularly pertinent when the sub-themes were revised to reflect the influence of pre-existing work-related issues and the subsequent influence of wellbeing promotion.

An issue proponents of RTA may realise when writing up their analysis is the potential for incongruence between traditional conventions for report writing and the appropriate style for reporting RTA—particularly when adopting an analytical approach to reporting on data. The document structure for academic journal articles and Masters or PhD theses typically subscribe to the convention of reporting results of analyses in a ‘results’ section and then synthesising and contextualising the results of analyses in a ‘discussion’ section. Conversely, Braun and Clarke recommend synthesising and contextualising data as and when they are reported in the ‘results’ section (Braun and Clarke 2013 ; Terry et al. 2017 ). This is a significant departure from the traditional reporting convention, which researchers—particularly post-graduate students—may find difficult to reconcile. While Braun and Clarke do not explicitly address this potential issue, it is implicitly evident that they would advocate that researchers prioritise the appropriate reporting style for RTA and not cede to the traditional reporting convention.

4 Conclusion

Although Braun and Clarke are widely published on the topic of reflexive thematic analysis, confusion persists in the wider literature regarding the appropriate implementation of this approach. The aim of this paper has been to contribute to dispelling some of this confusion by provide a worked example of Braun and Clarke’s contemporary approach to reflexive thematic analysis. To this end, this paper provided instruction in how to address the theoretical underpinnings of RTA by operationalising the theoretical assumptions of the example data in relation to the study from which the data was taken. Clear instruction was also provided in how to conduct a reflexive thematic analysis. This was achieved by providing a detailed step-by-step guide to Braun and Clarke’s six-phase process, and by providing numerous examples of the implementation of each phase based on my own research. Braun and Clarke have made (and continue to make) an extremely valuable contribution to the discourse regarding qualitative analysis. I strongly recommended that any prospective proponents of RTA who may read this paper thoroughly examine Braun and Clarke’s full body of literature in this area, and aim to achieve an understanding of RTA’s nuanced position among the numerous different approaches to thematic analysis.

While the reconceptualisation of RTA as falling within the remit of a purely qualitative paradigm precipitates that the research fall on the constructionist end of this continuum, it is nevertheless good practice to explicate this theoretical position.

Boyatzis, R.E.: Transforming Qualitative Information: Thematic Analysis and Code Development. Sage Publications, Thousand Oaks (1998)

Google Scholar  

Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa

Article   Google Scholar  

Braun, V., Clarke, V.: Thematic analysis. In: Cooper, H., Camic, P.M., Long, D.L., Panter, A.T., Rindskopf, D., Sher, K.J. (eds.) APA Handbook of Research Methods in Psychology, Research Designs, vol. 2, pp. 57–71. American Psychological Association, Washington (2012)

Braun, V., Clarke, V.: Successful Qualitative Research: A Practical Guide for Beginners. Sage Publications, Thousand Oaks (2013)

Braun, V., Clarke, V.: Thematic analysis. In: Teo, T. (ed.) Encyclopedia of Critical Psychology, pp. 1947–1952. Springer, New York (2014)

Braun, V., Clarke, V.: Reflecting on reflexive thematic analysis. Qual. Res. Sport Exerc. Health 11 (4), 589–597 (2019). https://doi.org/10.1080/2159676X.2019.1628806

Braun, V., Clarke, V.: One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qual. Res. Psychol. (2020). https://doi.org/10.1080/14780887.2020.1769238

Braun, V., Clarke, V., Weate, P.: Using Thematic Analysis in sport and exercise research. In: Smith, B., Sparkes, A.C. (eds.) Routledge Handbook of Qualitative Research in Sport and Exercise, pp. 191–205. Routledge, London (2016)

Braun, V., Clarke, V., Terry, G., Hayfield, N.: Thematic analysis. In: Liamputtong, P. (ed.) Handbook of Research Methods in Health and Social Sciences, pp. 843–860. Springer, Singapore (2018)

Braun, V., Clarke, V., Hayfield, N., Terry, G.: Answers to frequently asked questions about thematic analysis (2019). Retrieved from https://cdn.auckland.ac.nz/assets/psych/about/our-research/documents/Answers%20to%20frequently%20asked%20questions%20about%20thematic%20analysis%20April%202019.pdf

Burr, V.: An Introduction to Social Constructionism. Routledge, London, UK (1995)

Book   Google Scholar  

Clarke, V., Braun, V.: Thematic Analysis. In: Lyons, E., Coyle, A. (eds.) Analysing Qualitative Data in Psychology, 2nd edn., pp. 84–103. Sage Publications, London (2016)

Frith, H., Gleeson, K.: Clothing and embodiment: men managing body image and appearance. Psychol. Men Mascul. 5 (1), 40–48 (2004). https://doi.org/10.1037/1524-9220.5.1.40

Joffe, H.: Thematic analysis. In: Harper, D., Thompson, A.R. (eds.) Qualitative Research Methods in Mental Health and Psychotherapy: A Guide for Students and Practitioners, pp. 209–223. Wiley, Chichester (2012)

King, N., Brooks, J.M.: Template analysis for business and management students. Sage Publications, London, UK (2017)

Patton, M.Q.: Qualitative Evaluation and Research Methods, 2nd edn. Sage Publications, Thousand Oaks (1990)

Schwandt, T.A.: Constructivist, interpretivist approaches to human inquiry. In: Denzin, N.K., Lincoln, Y.S. (eds.) The Landscape of Qualitative Research: Theories and Issues, pp. 221–259. Sage Publications, Thousand Oaks (1998)

Smith, J., Firth, J.: Qualitative data analysis: The framework approach. Nurse Res. 18 (2), 52–62 (2011). https://doi.org/10.7748/nr2011.01.18.2.52.c8284

Terry, G., Hayfield, N., Braun, V., Clarke, V.: Thematic analysis. In: Willig, C., Rogers, W.S. (eds.) The SAGE Handbook of Qualitative Research in Psychology, pp. 17–37. Sage Publications, London (2017)

Chapter   Google Scholar  

Vaismoradi, M., Turunen, H., Bondas, T.: Content analysis and thematic analysis: implications for conducting a qualitative descriptive study. Nurs. Health Sci. 15 (3), 398–405 (2013). https://doi.org/10.1111/nhs.12048

Widdicombe, S., Wooffitt, R.: The Language of Youth Subcultures: Social Identity in Action. Harvester, Hemel Hempstead (1995)

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Byrne, D. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Qual Quant 56 , 1391–1412 (2022). https://doi.org/10.1007/s11135-021-01182-y

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Accepted : 06 June 2021

Published : 26 June 2021

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DOI : https://doi.org/10.1007/s11135-021-01182-y

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