types of study design in qualitative research

Chapter 2. Research Design

Getting started.

When I teach undergraduates qualitative research methods, the final product of the course is a “research proposal” that incorporates all they have learned and enlists the knowledge they have learned about qualitative research methods in an original design that addresses a particular research question. I highly recommend you think about designing your own research study as you progress through this textbook. Even if you don’t have a study in mind yet, it can be a helpful exercise as you progress through the course. But how to start? How can one design a research study before they even know what research looks like? This chapter will serve as a brief overview of the research design process to orient you to what will be coming in later chapters. Think of it as a “skeleton” of what you will read in more detail in later chapters. Ideally, you will read this chapter both now (in sequence) and later during your reading of the remainder of the text. Do not worry if you have questions the first time you read this chapter. Many things will become clearer as the text advances and as you gain a deeper understanding of all the components of good qualitative research. This is just a preliminary map to get you on the right road.

Research Design Steps

Before you even get started, you will need to have a broad topic of interest in mind. [1] . In my experience, students can confuse this broad topic with the actual research question, so it is important to clearly distinguish the two. And the place to start is the broad topic. It might be, as was the case with me, working-class college students. But what about working-class college students? What’s it like to be one? Why are there so few compared to others? How do colleges assist (or fail to assist) them? What interested me was something I could barely articulate at first and went something like this: “Why was it so difficult and lonely to be me?” And by extension, “Did others share this experience?”

Once you have a general topic, reflect on why this is important to you. Sometimes we connect with a topic and we don’t really know why. Even if you are not willing to share the real underlying reason you are interested in a topic, it is important that you know the deeper reasons that motivate you. Otherwise, it is quite possible that at some point during the research, you will find yourself turned around facing the wrong direction. I have seen it happen many times. The reason is that the research question is not the same thing as the general topic of interest, and if you don’t know the reasons for your interest, you are likely to design a study answering a research question that is beside the point—to you, at least. And this means you will be much less motivated to carry your research to completion.

Researcher Note

Why do you employ qualitative research methods in your area of study? What are the advantages of qualitative research methods for studying mentorship?

Qualitative research methods are a huge opportunity to increase access, equity, inclusion, and social justice. Qualitative research allows us to engage and examine the uniquenesses/nuances within minoritized and dominant identities and our experiences with these identities. Qualitative research allows us to explore a specific topic, and through that exploration, we can link history to experiences and look for patterns or offer up a unique phenomenon. There’s such beauty in being able to tell a particular story, and qualitative research is a great mode for that! For our work, we examined the relationships we typically use the term mentorship for but didn’t feel that was quite the right word. Qualitative research allowed us to pick apart what we did and how we engaged in our relationships, which then allowed us to more accurately describe what was unique about our mentorship relationships, which we ultimately named liberationships ( McAloney and Long 2021) . Qualitative research gave us the means to explore, process, and name our experiences; what a powerful tool!

How do you come up with ideas for what to study (and how to study it)? Where did you get the idea for studying mentorship?

Coming up with ideas for research, for me, is kind of like Googling a question I have, not finding enough information, and then deciding to dig a little deeper to get the answer. The idea to study mentorship actually came up in conversation with my mentorship triad. We were talking in one of our meetings about our relationship—kind of meta, huh? We discussed how we felt that mentorship was not quite the right term for the relationships we had built. One of us asked what was different about our relationships and mentorship. This all happened when I was taking an ethnography course. During the next session of class, we were discussing auto- and duoethnography, and it hit me—let’s explore our version of mentorship, which we later went on to name liberationships ( McAloney and Long 2021 ). The idea and questions came out of being curious and wanting to find an answer. As I continue to research, I see opportunities in questions I have about my work or during conversations that, in our search for answers, end up exposing gaps in the literature. If I can’t find the answer already out there, I can study it.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

When you have a better idea of why you are interested in what it is that interests you, you may be surprised to learn that the obvious approaches to the topic are not the only ones. For example, let’s say you think you are interested in preserving coastal wildlife. And as a social scientist, you are interested in policies and practices that affect the long-term viability of coastal wildlife, especially around fishing communities. It would be natural then to consider designing a research study around fishing communities and how they manage their ecosystems. But when you really think about it, you realize that what interests you the most is how people whose livelihoods depend on a particular resource act in ways that deplete that resource. Or, even deeper, you contemplate the puzzle, “How do people justify actions that damage their surroundings?” Now, there are many ways to design a study that gets at that broader question, and not all of them are about fishing communities, although that is certainly one way to go. Maybe you could design an interview-based study that includes and compares loggers, fishers, and desert golfers (those who golf in arid lands that require a great deal of wasteful irrigation). Or design a case study around one particular example where resources were completely used up by a community. Without knowing what it is you are really interested in, what motivates your interest in a surface phenomenon, you are unlikely to come up with the appropriate research design.

These first stages of research design are often the most difficult, but have patience . Taking the time to consider why you are going to go through a lot of trouble to get answers will prevent a lot of wasted energy in the future.

There are distinct reasons for pursuing particular research questions, and it is helpful to distinguish between them.  First, you may be personally motivated.  This is probably the most important and the most often overlooked.   What is it about the social world that sparks your curiosity? What bothers you? What answers do you need in order to keep living? For me, I knew I needed to get a handle on what higher education was for before I kept going at it. I needed to understand why I felt so different from my peers and whether this whole “higher education” thing was “for the likes of me” before I could complete my degree. That is the personal motivation question. Your personal motivation might also be political in nature, in that you want to change the world in a particular way. It’s all right to acknowledge this. In fact, it is better to acknowledge it than to hide it.

There are also academic and professional motivations for a particular study.  If you are an absolute beginner, these may be difficult to find. We’ll talk more about this when we discuss reviewing the literature. Simply put, you are probably not the only person in the world to have thought about this question or issue and those related to it. So how does your interest area fit into what others have studied? Perhaps there is a good study out there of fishing communities, but no one has quite asked the “justification” question. You are motivated to address this to “fill the gap” in our collective knowledge. And maybe you are really not at all sure of what interests you, but you do know that [insert your topic] interests a lot of people, so you would like to work in this area too. You want to be involved in the academic conversation. That is a professional motivation and a very important one to articulate.

Practical and strategic motivations are a third kind. Perhaps you want to encourage people to take better care of the natural resources around them. If this is also part of your motivation, you will want to design your research project in a way that might have an impact on how people behave in the future. There are many ways to do this, one of which is using qualitative research methods rather than quantitative research methods, as the findings of qualitative research are often easier to communicate to a broader audience than the results of quantitative research. You might even be able to engage the community you are studying in the collecting and analyzing of data, something taboo in quantitative research but actively embraced and encouraged by qualitative researchers. But there are other practical reasons, such as getting “done” with your research in a certain amount of time or having access (or no access) to certain information. There is nothing wrong with considering constraints and opportunities when designing your study. Or maybe one of the practical or strategic goals is about learning competence in this area so that you can demonstrate the ability to conduct interviews and focus groups with future employers. Keeping that in mind will help shape your study and prevent you from getting sidetracked using a technique that you are less invested in learning about.

STOP HERE for a moment

I recommend you write a paragraph (at least) explaining your aims and goals. Include a sentence about each of the following: personal/political goals, practical or professional/academic goals, and practical/strategic goals. Think through how all of the goals are related and can be achieved by this particular research study . If they can’t, have a rethink. Perhaps this is not the best way to go about it.

You will also want to be clear about the purpose of your study. “Wait, didn’t we just do this?” you might ask. No! Your goals are not the same as the purpose of the study, although they are related. You can think about purpose lying on a continuum from “ theory ” to “action” (figure 2.1). Sometimes you are doing research to discover new knowledge about the world, while other times you are doing a study because you want to measure an impact or make a difference in the world.

Basic research involves research that is done for the sake of “pure” knowledge—that is, knowledge that, at least at this moment in time, may not have any apparent use or application. Often, and this is very important, knowledge of this kind is later found to be extremely helpful in solving problems. So one way of thinking about basic research is that it is knowledge for which no use is yet known but will probably one day prove to be extremely useful. If you are doing basic research, you do not need to argue its usefulness, as the whole point is that we just don’t know yet what this might be.

Researchers engaged in basic research want to understand how the world operates. They are interested in investigating a phenomenon to get at the nature of reality with regard to that phenomenon. The basic researcher’s purpose is to understand and explain ( Patton 2002:215 ).

Basic research is interested in generating and testing hypotheses about how the world works. Grounded Theory is one approach to qualitative research methods that exemplifies basic research (see chapter 4). Most academic journal articles publish basic research findings. If you are working in academia (e.g., writing your dissertation), the default expectation is that you are conducting basic research.

Applied research in the social sciences is research that addresses human and social problems. Unlike basic research, the researcher has expectations that the research will help contribute to resolving a problem, if only by identifying its contours, history, or context. From my experience, most students have this as their baseline assumption about research. Why do a study if not to make things better? But this is a common mistake. Students and their committee members are often working with default assumptions here—the former thinking about applied research as their purpose, the latter thinking about basic research: “The purpose of applied research is to contribute knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment. While in basic research the source of questions is the tradition within a scholarly discipline, in applied research the source of questions is in the problems and concerns experienced by people and by policymakers” ( Patton 2002:217 ).

Applied research is less geared toward theory in two ways. First, its questions do not derive from previous literature. For this reason, applied research studies have much more limited literature reviews than those found in basic research (although they make up for this by having much more “background” about the problem). Second, it does not generate theory in the same way as basic research does. The findings of an applied research project may not be generalizable beyond the boundaries of this particular problem or context. The findings are more limited. They are useful now but may be less useful later. This is why basic research remains the default “gold standard” of academic research.

Evaluation research is research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems. We already know the problems, and someone has already come up with solutions. There might be a program, say, for first-generation college students on your campus. Does this program work? Are first-generation students who participate in the program more likely to graduate than those who do not? These are the types of questions addressed by evaluation research. There are two types of research within this broader frame; however, one more action-oriented than the next. In summative evaluation , an overall judgment about the effectiveness of a program or policy is made. Should we continue our first-gen program? Is it a good model for other campuses? Because the purpose of such summative evaluation is to measure success and to determine whether this success is scalable (capable of being generalized beyond the specific case), quantitative data is more often used than qualitative data. In our example, we might have “outcomes” data for thousands of students, and we might run various tests to determine if the better outcomes of those in the program are statistically significant so that we can generalize the findings and recommend similar programs elsewhere. Qualitative data in the form of focus groups or interviews can then be used for illustrative purposes, providing more depth to the quantitative analyses. In contrast, formative evaluation attempts to improve a program or policy (to help “form” or shape its effectiveness). Formative evaluations rely more heavily on qualitative data—case studies, interviews, focus groups. The findings are meant not to generalize beyond the particular but to improve this program. If you are a student seeking to improve your qualitative research skills and you do not care about generating basic research, formative evaluation studies might be an attractive option for you to pursue, as there are always local programs that need evaluation and suggestions for improvement. Again, be very clear about your purpose when talking through your research proposal with your committee.

Action research takes a further step beyond evaluation, even formative evaluation, to being part of the solution itself. This is about as far from basic research as one could get and definitely falls beyond the scope of “science,” as conventionally defined. The distinction between action and research is blurry, the research methods are often in constant flux, and the only “findings” are specific to the problem or case at hand and often are findings about the process of intervention itself. Rather than evaluate a program as a whole, action research often seeks to change and improve some particular aspect that may not be working—maybe there is not enough diversity in an organization or maybe women’s voices are muted during meetings and the organization wonders why and would like to change this. In a further step, participatory action research , those women would become part of the research team, attempting to amplify their voices in the organization through participation in the action research. As action research employs methods that involve people in the process, focus groups are quite common.

If you are working on a thesis or dissertation, chances are your committee will expect you to be contributing to fundamental knowledge and theory ( basic research ). If your interests lie more toward the action end of the continuum, however, it is helpful to talk to your committee about this before you get started. Knowing your purpose in advance will help avoid misunderstandings during the later stages of the research process!

The Research Question

Once you have written your paragraph and clarified your purpose and truly know that this study is the best study for you to be doing right now , you are ready to write and refine your actual research question. Know that research questions are often moving targets in qualitative research, that they can be refined up to the very end of data collection and analysis. But you do have to have a working research question at all stages. This is your “anchor” when you get lost in the data. What are you addressing? What are you looking at and why? Your research question guides you through the thicket. It is common to have a whole host of questions about a phenomenon or case, both at the outset and throughout the study, but you should be able to pare it down to no more than two or three sentences when asked. These sentences should both clarify the intent of the research and explain why this is an important question to answer. More on refining your research question can be found in chapter 4.

Chances are, you will have already done some prior reading before coming up with your interest and your questions, but you may not have conducted a systematic literature review. This is the next crucial stage to be completed before venturing further. You don’t want to start collecting data and then realize that someone has already beaten you to the punch. A review of the literature that is already out there will let you know (1) if others have already done the study you are envisioning; (2) if others have done similar studies, which can help you out; and (3) what ideas or concepts are out there that can help you frame your study and make sense of your findings. More on literature reviews can be found in chapter 9.

In addition to reviewing the literature for similar studies to what you are proposing, it can be extremely helpful to find a study that inspires you. This may have absolutely nothing to do with the topic you are interested in but is written so beautifully or organized so interestingly or otherwise speaks to you in such a way that you want to post it somewhere to remind you of what you want to be doing. You might not understand this in the early stages—why would you find a study that has nothing to do with the one you are doing helpful? But trust me, when you are deep into analysis and writing, having an inspirational model in view can help you push through. If you are motivated to do something that might change the world, you probably have read something somewhere that inspired you. Go back to that original inspiration and read it carefully and see how they managed to convey the passion that you so appreciate.

At this stage, you are still just getting started. There are a lot of things to do before setting forth to collect data! You’ll want to consider and choose a research tradition and a set of data-collection techniques that both help you answer your research question and match all your aims and goals. For example, if you really want to help migrant workers speak for themselves, you might draw on feminist theory and participatory action research models. Chapters 3 and 4 will provide you with more information on epistemologies and approaches.

Next, you have to clarify your “units of analysis.” What is the level at which you are focusing your study? Often, the unit in qualitative research methods is individual people, or “human subjects.” But your units of analysis could just as well be organizations (colleges, hospitals) or programs or even whole nations. Think about what it is you want to be saying at the end of your study—are the insights you are hoping to make about people or about organizations or about something else entirely? A unit of analysis can even be a historical period! Every unit of analysis will call for a different kind of data collection and analysis and will produce different kinds of “findings” at the conclusion of your study. [2]

Regardless of what unit of analysis you select, you will probably have to consider the “human subjects” involved in your research. [3] Who are they? What interactions will you have with them—that is, what kind of data will you be collecting? Before answering these questions, define your population of interest and your research setting. Use your research question to help guide you.

Let’s use an example from a real study. In Geographies of Campus Inequality , Benson and Lee ( 2020 ) list three related research questions: “(1) What are the different ways that first-generation students organize their social, extracurricular, and academic activities at selective and highly selective colleges? (2) how do first-generation students sort themselves and get sorted into these different types of campus lives; and (3) how do these different patterns of campus engagement prepare first-generation students for their post-college lives?” (3).

Note that we are jumping into this a bit late, after Benson and Lee have described previous studies (the literature review) and what is known about first-generation college students and what is not known. They want to know about differences within this group, and they are interested in ones attending certain kinds of colleges because those colleges will be sites where academic and extracurricular pressures compete. That is the context for their three related research questions. What is the population of interest here? First-generation college students . What is the research setting? Selective and highly selective colleges . But a host of questions remain. Which students in the real world, which colleges? What about gender, race, and other identity markers? Will the students be asked questions? Are the students still in college, or will they be asked about what college was like for them? Will they be observed? Will they be shadowed? Will they be surveyed? Will they be asked to keep diaries of their time in college? How many students? How many colleges? For how long will they be observed?

Recommendation

Take a moment and write down suggestions for Benson and Lee before continuing on to what they actually did.

Have you written down your own suggestions? Good. Now let’s compare those with what they actually did. Benson and Lee drew on two sources of data: in-depth interviews with sixty-four first-generation students and survey data from a preexisting national survey of students at twenty-eight selective colleges. Let’s ignore the survey for our purposes here and focus on those interviews. The interviews were conducted between 2014 and 2016 at a single selective college, “Hilltop” (a pseudonym ). They employed a “purposive” sampling strategy to ensure an equal number of male-identifying and female-identifying students as well as equal numbers of White, Black, and Latinx students. Each student was interviewed once. Hilltop is a selective liberal arts college in the northeast that enrolls about three thousand students.

How did your suggestions match up to those actually used by the researchers in this study? It is possible your suggestions were too ambitious? Beginning qualitative researchers can often make that mistake. You want a research design that is both effective (it matches your question and goals) and doable. You will never be able to collect data from your entire population of interest (unless your research question is really so narrow to be relevant to very few people!), so you will need to come up with a good sample. Define the criteria for this sample, as Benson and Lee did when deciding to interview an equal number of students by gender and race categories. Define the criteria for your sample setting too. Hilltop is typical for selective colleges. That was a research choice made by Benson and Lee. For more on sampling and sampling choices, see chapter 5.

Benson and Lee chose to employ interviews. If you also would like to include interviews, you have to think about what will be asked in them. Most interview-based research involves an interview guide, a set of questions or question areas that will be asked of each participant. The research question helps you create a relevant interview guide. You want to ask questions whose answers will provide insight into your research question. Again, your research question is the anchor you will continually come back to as you plan for and conduct your study. It may be that once you begin interviewing, you find that people are telling you something totally unexpected, and this makes you rethink your research question. That is fine. Then you have a new anchor. But you always have an anchor. More on interviewing can be found in chapter 11.

Let’s imagine Benson and Lee also observed college students as they went about doing the things college students do, both in the classroom and in the clubs and social activities in which they participate. They would have needed a plan for this. Would they sit in on classes? Which ones and how many? Would they attend club meetings and sports events? Which ones and how many? Would they participate themselves? How would they record their observations? More on observation techniques can be found in both chapters 13 and 14.

At this point, the design is almost complete. You know why you are doing this study, you have a clear research question to guide you, you have identified your population of interest and research setting, and you have a reasonable sample of each. You also have put together a plan for data collection, which might include drafting an interview guide or making plans for observations. And so you know exactly what you will be doing for the next several months (or years!). To put the project into action, there are a few more things necessary before actually going into the field.

First, you will need to make sure you have any necessary supplies, including recording technology. These days, many researchers use their phones to record interviews. Second, you will need to draft a few documents for your participants. These include informed consent forms and recruiting materials, such as posters or email texts, that explain what this study is in clear language. Third, you will draft a research protocol to submit to your institutional review board (IRB) ; this research protocol will include the interview guide (if you are using one), the consent form template, and all examples of recruiting material. Depending on your institution and the details of your study design, it may take weeks or even, in some unfortunate cases, months before you secure IRB approval. Make sure you plan on this time in your project timeline. While you wait, you can continue to review the literature and possibly begin drafting a section on the literature review for your eventual presentation/publication. More on IRB procedures can be found in chapter 8 and more general ethical considerations in chapter 7.

Once you have approval, you can begin!

Research Design Checklist

Before data collection begins, do the following:

• Write a paragraph explaining your aims and goals (personal/political, practical/strategic, professional/academic).
• Define your research question; write two to three sentences that clarify the intent of the research and why this is an important question to answer.
• Review the literature for similar studies that address your research question or similar research questions; think laterally about some literature that might be helpful or illuminating but is not exactly about the same topic.
• Find a written study that inspires you—it may or may not be on the research question you have chosen.
• Consider and choose a research tradition and set of data-collection techniques that (1) help answer your research question and (2) match your aims and goals.
• Define your population of interest and your research setting.
• Define the criteria for your sample (How many? Why these? How will you find them, gain access, and acquire consent?).
• If you are conducting interviews, draft an interview guide.
•  If you are making observations, create a plan for observations (sites, times, recording, access).
• Acquire any necessary technology (recording devices/software).
• Draft consent forms that clearly identify the research focus and selection process.
• Create recruiting materials (posters, email, texts).
• Apply for IRB approval (proposal plus consent form plus recruiting materials).
• Block out time for collecting data.
• At the end of the chapter, you will find a " Research Design Checklist " that summarizes the main recommendations made here ↵
• For example, if your focus is society and culture , you might collect data through observation or a case study. If your focus is individual lived experience , you are probably going to be interviewing some people. And if your focus is language and communication , you will probably be analyzing text (written or visual). ( Marshall and Rossman 2016:16 ). ↵
• You may not have any "live" human subjects. There are qualitative research methods that do not require interactions with live human beings - see chapter 16 , "Archival and Historical Sources." But for the most part, you are probably reading this textbook because you are interested in doing research with people. The rest of the chapter will assume this is the case. ↵

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A methodological tradition of inquiry and research design that focuses on an individual case (e.g., setting, institution, or sometimes an individual) in order to explore its complexity, history, and interactive parts.  As an approach, it is particularly useful for obtaining a deep appreciation of an issue, event, or phenomenon of interest in its particular context.

The controlling force in research; can be understood as lying on a continuum from basic research (knowledge production) to action research (effecting change).

In its most basic sense, a theory is a story we tell about how the world works that can be tested with empirical evidence.  In qualitative research, we use the term in a variety of ways, many of which are different from how they are used by quantitative researchers.  Although some qualitative research can be described as “testing theory,” it is more common to “build theory” from the data using inductive reasoning , as done in Grounded Theory .  There are so-called “grand theories” that seek to integrate a whole series of findings and stories into an overarching paradigm about how the world works, and much smaller theories or concepts about particular processes and relationships.  Theory can even be used to explain particular methodological perspectives or approaches, as in Institutional Ethnography , which is both a way of doing research and a theory about how the world works.

Research that is interested in generating and testing hypotheses about how the world works.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

Research that contributes knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment.

Research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems.  There are two kinds: summative and formative .

Research in which an overall judgment about the effectiveness of a program or policy is made, often for the purpose of generalizing to other cases or programs.  Generally uses qualitative research as a supplement to primary quantitative data analyses.  Contrast formative evaluation research .

Research designed to improve a program or policy (to help “form” or shape its effectiveness); relies heavily on qualitative research methods.  Contrast summative evaluation research

Research carried out at a particular organizational or community site with the intention of affecting change; often involves research subjects as participants of the study.  See also participatory action research .

Research in which both researchers and participants work together to understand a problematic situation and change it for the better.

The level of the focus of analysis (e.g., individual people, organizations, programs, neighborhoods).

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A fictional name assigned to give anonymity to a person, group, or place.  Pseudonyms are important ways of protecting the identity of research participants while still providing a “human element” in the presentation of qualitative data.  There are ethical considerations to be made in selecting pseudonyms; some researchers allow research participants to choose their own.

A requirement for research involving human participants; the documentation of informed consent.  In some cases, oral consent or assent may be sufficient, but the default standard is a single-page easy-to-understand form that both the researcher and the participant sign and date.   Under federal guidelines, all researchers "shall seek such consent only under circumstances that provide the prospective subject or the representative sufficient opportunity to consider whether or not to participate and that minimize the possibility of coercion or undue influence. The information that is given to the subject or the representative shall be in language understandable to the subject or the representative.  No informed consent, whether oral or written, may include any exculpatory language through which the subject or the representative is made to waive or appear to waive any of the subject's rights or releases or appears to release the investigator, the sponsor, the institution, or its agents from liability for negligence" (21 CFR 50.20).  Your IRB office will be able to provide a template for use in your study .

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much . It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Research Paradigms 

• Positivist versus Post-Positivist
• Social Constructivist (this paradigm/ideology mostly birth qualitative studies)

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

CMU Libraries is committed to helping members of our community become data experts. To that end, CMU is offering public facing workshops that discuss Qualitative Research, Coding, and Community Engagement best practices.

The following workshops are a part of a broader series on using data. Please follow the links to register for the events. 

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design  

Upcoming Event: March 21st, 2024 (12:00pm -1:00 pm)

Community Engagement and Collaboration Event 

Join us for an event to improve, build on and expand the connections between Carnegie Mellon University resources and the Pittsburgh community. CMU resources such as the Libraries and Sustainability Initiative can be leveraged by users not affiliated with the university, but barriers can prevent them from fully engaging.

The conversation features representatives from CMU departments and local organizations about the community engagement efforts currently underway at CMU and opportunities to improve upon them. Speakers will highlight current and ongoing projects and share resources to support future collaboration.

Event Moderators:

Taiwo Lasisi, CLIR Postdoctoral Fellow in Community Data Literacy,  Carnegie Mellon University Libraries

Emma Slayton, Data Curation, Visualization, & GIS Specialist,  Carnegie Mellon University Libraries

Nicky Agate , Associate Dean for Academic Engagement, Carnegie Mellon University Libraries

Chelsea Cohen , The University’s Executive fellow for community engagement, Carnegie Mellon University

Sarah Ceurvorst , Academic Pathways Manager, Program Director, LEAP (Leadership, Excellence, Access, Persistence) Carnegie Mellon University

Julia Poeppibg , Associate Director of Partnership Development, Information Systems, Carnegie Mellon University 

Scott Wolovich , Director of New Sun Rising, Pittsburgh 

Additional workshops and events will be forthcoming. Watch this space for updates. 

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

Qualitative research adopts numerous methods or techniques including interviews, focus groups, and observation. Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant observers to share the experiences of the subject or non-participant or detached observers.

What constitutes a good research question? Does the question drive research design choices?

According to Doody and Bailey (2014);

 We can only develop a good research question by consulting relevant literature, colleagues, and supervisors experienced in the area of research. (inductive interactions).

Helps to have a directed research aim and objective.

Researchers should not be “ research trendy” and have enough evidence. This is why research objectives are important. It helps to take time, and resources into consideration.

Research questions can be developed from theoretical knowledge, previous research or experience, or a practical need at work (Parahoo 2014). They have numerous roles, such as identifying the importance of the research and providing clarity of purpose for the research, in terms of what the research intends to achieve in the end.

Qualitative Research Questions

What constitutes a good Qualitative research question?

A good qualitative question answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. Qualitative research gathers participants' experiences, perceptions and behavior.

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library? 

How does it feel to be a first-generation student attending college?

Difference example (between Qualitative and Quantitative research questions):

How many college students signed up for the new semester? (Quan) 

How do college students feel about the new semester? What are their experiences so far? (Qual)

• Qualitative Research Design Workshop Powerpoint

Foley G, Timonen V. Using Grounded Theory Method to Capture and Analyze Health Care Experiences. Health Serv Res. 2015 Aug;50(4):1195-210. [ PMC free article: PMC4545354 ] [ PubMed: 25523315 ]

Devers KJ. How will we know "good" qualitative research when we see it? Beginning the dialogue in health services research. Health Serv Res. 1999 Dec;34(5 Pt 2):1153-88. [ PMC free article: PMC1089058 ] [ PubMed: 10591278 ]

Huston P, Rowan M. Qualitative studies. Their role in medical research. Can Fam Physician. 1998 Nov;44:2453-8. [ PMC free article: PMC2277956 ] [ PubMed: 9839063 ]

Corner EJ, Murray EJ, Brett SJ. Qualitative, grounded theory exploration of patients' experience of early mobilisation, rehabilitation and recovery after critical illness. BMJ Open. 2019 Feb 24;9(2):e026348. [ PMC free article: PMC6443050 ] [ PubMed: 30804034 ]

Moser A, Korstjens I. Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract. 2018 Dec;24(1):9-18. [ PMC free article: PMC5774281 ] [ PubMed: 29199486 ]

Houghton C, Murphy K, Meehan B, Thomas J, Brooker D, Casey D. From screening to synthesis: using nvivo to enhance transparency in qualitative evidence synthesis. J Clin Nurs. 2017 Mar;26(5-6):873-881. [ PubMed: 27324875 ]

Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: Potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. [ PubMed: 32321144 ]

Zamawe FC. The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi Med J. 2015 Mar;27(1):13-5. [ PMC free article: PMC4478399 ] [ PubMed: 26137192 ]

Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract. 2018 Dec;24(1):120-124. [ PMC free article: PMC8816392 ] [ PubMed: 29202616 ]

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-51. [ PubMed: 24979285 ]

Palermo C, King O, Brock T, Brown T, Crampton P, Hall H, Macaulay J, Morphet J, Mundy M, Oliaro L, Paynter S, Williams B, Wright C, E Rees C. Setting priorities for health education research: A mixed methods study. Med Teach. 2019 Sep;41(9):1029-1038. [ PubMed: 31141390 ]

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9.4 Types of qualitative research designs

Learning objectives.

• Define focus groups and outline how they differ from one-on-one interviews
• Describe how to determine the best size for focus groups
• Identify the important considerations in focus group composition
• Discuss how to moderate focus groups
• Identify the strengths and weaknesses of focus group methodology
• Describe case study research, ethnography, and phenomenology.

There are various types of approaches to qualitative research.  This chapter presents information about focus groups, which are often used in social work research.  It also introduces case studies, ethnography, and phenomenology.

Focus Groups

Focus groups resemble qualitative interviews in that a researcher may prepare a guide in advance and interact with participants by asking them questions. But anyone who has conducted both one-on-one interviews and focus groups knows that each is unique. In an interview, usually one member (the research participant) is most active while the other (the researcher) plays the role of listener, conversation guider, and question-asker. Focus groups , on the other hand, are planned discussions designed to elicit group interaction and “obtain perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger & Casey, 2000, p. 5).  In focus groups, the researcher play a different role than in a one-on-one interview. The researcher’s aim is to get participants talking to each other,  to observe interactions among participants, and moderate the discussion.

There are numerous examples of focus group research. In their 2008 study, for example, Amy Slater and Marika Tiggemann (2010) conducted six focus groups with 49 adolescent girls between the ages of 13 and 15 to learn more about girls’ attitudes towards’ participation in sports. In order to get focus group participants to speak with one another rather than with the group facilitator, the focus group interview guide contained just two questions: “Can you tell me some of the reasons that girls stop playing sports or other physical activities?” and “Why do you think girls don’t play as much sport/physical activity as boys?” In another focus group study, Virpi Ylanne and Angie Williams (2009) held nine focus group sessions with adults of different ages to gauge their perceptions of how older characters are represented in television commercials. Among other considerations, the researchers were interested in discovering how focus group participants position themselves and others in terms of age stereotypes and identities during the group discussion. In both examples, the researchers’ core interest in group interaction could not have been assessed had interviews been conducted on a one-on-one basis, making the focus group method an ideal choice.

Who should be in your focus group?

In some ways, focus groups require more planning than other qualitative methods of data collection, such as one-on-one interviews in which a researcher may be better able to the dialogue. Researchers must take care to form focus groups with members who will want to interact with one another and to control the timing of the event so that participants are not asked nor expected to stay for a longer time than they’ve agreed to participate. The researcher should also be prepared to inform focus group participants of their responsibility to maintain the confidentiality of what is said in the group. But while the researcher can and should encourage all focus group members to maintain confidentiality, she should also clarify to participants that the unique nature of the group setting prevents her from being able to promise that confidentiality will be maintained by other participants. Once focus group members leave the research setting, researchers cannot control what they say to other people.

Group size should be determined in part by the topic of the interview and your sense of the likelihood that participants will have much to say without much prompting. If the topic is one about which you think participants feel passionately and will have much to say, a group of 3–5 could make sense. Groups larger than that, especially for heated topics, can easily become unmanageable. Some researchers say that a group of about 6–10 participants is the ideal size for focus group research (Morgan, 1997); others recommend that groups should include 3–12 participants (Adler & Clark, 2008).  The size of the focus group is ultimately the decision of the researcher. When forming groups and deciding how large or small to make them, take into consideration what you know about the topic and participants’ potential interest in, passion for, and feelings about the topic. Also consider your comfort level and experience in conducting focus groups. These factors will help you decide which size is right in your particular case.

It may seem counterintuitive, but in general, it is better to form focus groups consisting of participants who do not know one another than to create groups consisting of friends, relatives, or acquaintances (Agar & MacDonald, 1995).  The reason is that group members who know each other may not share some taken-for-granted knowledge or assumptions. In research, it is precisely the  taken-for-granted knowledge that is often of interest; thus, the focus group researcher should avoid setting up interactions where participants may be discouraged to question or raise issues that they take for granted. However, group members should not be so different from one another that participants will be unlikely to feel comfortable talking with one another.

Focus group researchers must carefully consider the composition of the groups they put together. In his text on conducting focus groups, Morgan (1997) suggests that “homogeneity in background and not homogeneity in attitudes” (p. 36) should be the goal, since participants must feel comfortable speaking up but must also have enough differences to facilitate a productive discussion.  Whatever composition a researcher designs for her focus groups, the important point to keep in mind is that focus group dynamics are shaped by multiple social contexts (Hollander, 2004). Participants’ silences as well as their speech may be shaped by gender, race, class, sexuality, age, or other background characteristics or social dynamics—all of which might be suppressed or exacerbated depending on the composition of the group. Hollander (2004) suggests that researchers must pay careful attention to group composition, must be attentive to group dynamics during the focus group discussion, and should use multiple methods of data collection in order to “untangle participants’ responses and their relationship to the social contexts of the focus group” (p. 632).

The role of the moderator

In addition to the importance of group composition, focus groups also require skillful moderation. A moderator is the researcher tasked with facilitating the conversation in the focus group. Participants may ask each other follow-up questions, agree or disagree with one another, display body language that tells us something about their feelings about the conversation, or even come up with questions not previously conceived of by the researcher. It is just these sorts of interactions and displays that are of interest to the researcher. A researcher conducting focus groups collects data on more than people’s direct responses to her question, as in interviews.

The moderator’s job is not to ask questions to each person individually, but to stimulate conversation between participants. It is important to set ground rules for focus groups at the outset of the discussion. Remind participants you’ve invited them to participate because you want to hear from all of them. Therefore, the group should aim to let just one person speak at a time and avoid letting just a couple of participants dominate the conversation. One way to do this is to begin the discussion by asking participants to briefly introduce themselves or to provide a brief response to an opening question. This will help set the tone of having all group members participate. Also, ask participants to avoid having side conversations; thoughts or reactions to what is said in the group are important and should be shared with everyone.

As the focus group gets rolling, the moderator will play a less active role as participants talk to one another. There may be times when the conversation stagnates or when you, as moderator, wish to guide the conversation in another direction. In these instances, it is important to demonstrate that you’ve been paying attention to what participants have said. Being prepared to interject statements or questions such as “I’d really like to hear more about what Sunil and Joe think about what Dominick and Jae have been saying” or “Several of you have mentioned X. What do others think about this?” will be important for keeping the conversation going. It can also help redirect the conversation, shift the focus to participants who have been less active in the group, and serve as a cue to those who may be dominating the conversation that it is time to allow others to speak. Researchers may choose to use multiple moderators to make managing these various tasks easier.

Moderators are often too busy working with participants to take diligent notes during a focus group. It is helpful to have a note-taker who can record participants’ responses (Liamputtong, 2011). The note-taker creates, in essence, the first draft of interpretation for the data in the study. They note themes in responses, nonverbal cues, and other information to be included in the analysis later on. Focus groups are analyzed in a similar way as interviews; however, the interactive dimension between participants adds another element to the analytical process. Researchers must attend to the group dynamics of each focus group, as “verbal and nonverbal expressions, the tactical use of humour, interruptions in interaction, and disagreement between participants” are all data that are vital to include in analysis (Liamputtong, 2011, p. 175). Note-takers record these elements in field notes, which allows moderators to focus on the conversation.

Strengths and weaknesses of focus groups

Focus groups share many of the strengths and weaknesses of one-on-one qualitative interviews. Both methods can yield very detailed, in-depth information; are excellent for studying social processes; and provide researchers with an opportunity not only to hear what participants say but also to observe what they do in terms of their body language. Focus groups offer the added benefit of giving researchers a chance to collect data on human interaction by observing how group participants respond and react to one another. Like one-on-one qualitative interviews, focus groups can also be quite expensive and time-consuming. However, there may be some savings with focus groups as it takes fewer group events than one-on-one interviews to gather data from the same number of people. Another potential drawback of focus groups, which is not a concern for one-on-one interviews, is that one or two participants might dominate the group, silencing other participants. Careful planning and skillful moderation on the part of the researcher are crucial for avoiding, or at least dealing with, such possibilities. The various strengths and weaknesses of focus group research are summarized in Table 91.

Grounded Theory

Grounded theory has been widely used since its development in the late 1960s (Glaser & Strauss, 1967). Largely derived from schools of sociology, grounded theory involves emersion of the researcher in the field and in the data. Researchers follow a systematic set of procedures and a simultaneous approach to data collection and analysis. Grounded theory is most often used to generate rich explanations of complex actions, processes, and transitions. The primary mode of data collection is one-on-one participant interviews. Sample sizes tend to range from 20 to 30 individuals, sampled purposively (Padgett, 2016). However, sample sizes can be larger or smaller, depending on data saturation. Data saturation is the point in the qualitative research data collection process when no new information is being discovered. Researchers use a constant comparative approach in which previously collected data are analyzed during the same time frame as new data are being collected.  This allows the researchers to determine when new information is no longer being gleaned from data collection and analysis — that data saturation has been reached — in order to conclude the data collection phase.

Rather than apply or test existing grand theories, or “Big T” theories, grounded theory focuses on “small t” theories (Padgett, 2016). Grand theories, or “Big T” theories, are systems of principles, ideas, and concepts used to predict phenomena. These theories are backed up by facts and tested hypotheses. “Small t” theories are speculative and contingent upon specific contexts. In grounded theory, these “small t” theories are grounded in events and experiences and emerge from the analysis of the data collected.

One notable application of grounded theory produced a “small t” theory of acceptance following cancer diagnoses (Jakobsson, Horvath, & Ahlberg, 2005). Using grounded theory, the researchers interviewed nine patients in western Sweden. Data collection and analysis stopped when saturation was reached. The researchers found that action and knowledge, given with respect and continuity led to confidence which led to acceptance. This “small t” theory continues to be applied and further explored in other contexts.

Case study research

Case study research is an intensive longitudinal study of a phenomenon at one or more research sites for the purpose of deriving detailed, contextualized inferences and understanding the dynamic process underlying a phenomenon of interest. Case research is a unique research design in that it can be used in an interpretive manner to build theories or in a positivist manner to test theories. The previous chapter on case research discusses both techniques in depth and provides illustrative exemplars. Furthermore, the case researcher is a neutral observer (direct observation) in the social setting rather than an active participant (participant observation). As with any other interpretive approach, drawing meaningful inferences from case research depends heavily on the observational skills and integrative abilities of the researcher.

Ethnography

The ethnographic research method, derived largely from the field of anthropology, emphasizes studying a phenomenon within the context of its culture. The researcher must be deeply immersed in the social culture over an extended period of time (usually 8 months to 2 years) and should engage, observe, and record the daily life of the studied culture and its social participants within their natural setting. The primary mode of data collection is participant observation, and data analysis involves a “sense-making” approach. In addition, the researcher must take extensive field notes, and narrate her experience in descriptive detail so that readers may experience the same culture as the researcher. In this method, the researcher has two roles: rely on her unique knowledge and engagement to generate insights (theory), and convince the scientific community of the trans-situational nature of the studied phenomenon.

The classic example of ethnographic research is Jane Goodall’s study of primate behaviors, where she lived with chimpanzees in their natural habitat at Gombe National Park in Tanzania, observed their behaviors, interacted with them, and shared their lives. During that process, she learnt and chronicled how chimpanzees seek food and shelter, how they socialize with each other, their communication patterns, their mating behaviors, and so forth. A more contemporary example of ethnographic research is Myra Bluebond-Langer’s (1996)14 study of decision making in families with children suffering from life-threatening illnesses, and the physical, psychological, environmental, ethical, legal, and cultural issues that influence such decision-making. The researcher followed the experiences of approximately 80 children with incurable illnesses and their families for a period of over two years. Data collection involved participant observation and formal/informal conversations with children, their parents and relatives, and health care providers to document their lived experience.

Phenomenology

Phenomenology is a research method that emphasizes the study of conscious experiences as a way of understanding the reality around us. Phenomenology is concerned with the systematic reflection and analysis of phenomena associated with conscious experiences, such as human judgment, perceptions, and actions, with the goal of (1) appreciating and describing social reality from the diverse subjective perspectives of the participants involved, and (2) understanding the symbolic meanings (“deep structure”) underlying these subjective experiences. Phenomenological inquiry requires that researchers eliminate any prior assumptions and personal biases, empathize with the participant’s situation, and tune into existential dimensions of that situation, so that they can fully understand the deep structures that drives the conscious thinking, feeling, and behavior of the studied participants.

Some researchers view phenomenology as a philosophy rather than as a research method. In response to this criticism, Giorgi and Giorgi (2003) developed an existential phenomenological research method to guide studies in this area. This method can be grouped into data collection and data analysis phases. In the data collection phase, participants embedded in a social phenomenon are interviewed to capture their subjective experiences and perspectives regarding the phenomenon under investigation. Examples of questions that may be asked include “can you describe a typical day” or “can you describe that particular incident in more detail?” These interviews are recorded and transcribed for further analysis. During data analysis, the researcher reads the transcripts to: (1) get a sense of the whole, and (2) establish “units of significance” that can faithfully represent participants’ subjective experiences. Examples of such units of significance are concepts such as “felt space” and “felt time,” which are then used to document participants’ psychological experiences. For instance, did participants feel safe, free, trapped, or joyous when experiencing a phenomenon (“felt-space”)? Did they feel that their experience was pressured, slow, or discontinuous (“felt-time”)? Phenomenological analysis should take into account the participants’ temporal landscape (i.e., their sense of past, present, and future), and the researcher must transpose herself in an imaginary sense in the participant’s situation (i.e., temporarily live the participant’s life). The participants’ lived experience is described in form of a narrative or using emergent themes. The analysis then delves into these themes to identify multiple layers of meaning while retaining the fragility and ambiguity of subjects’ lived experiences.

Key Takeaways

• In terms of focus group composition, homogeneity of background among participants is recommended while diverse attitudes within the group are ideal.
• The goal of a focus group is to get participants to talk with one another rather than the researcher.
• Like one-on-one qualitative interviews, focus groups can yield very detailed information, are excellent for studying social processes, and provide researchers with an opportunity to observe participants’ body language; they also allow researchers to observe social interaction.
• Focus groups can be expensive and time-consuming, as are one-on-one interviews; there is also the possibility that a few participants will dominate the group and silence others in the group.
• Other types of qualitative research include case studies, ethnography, and phenomenology.
• Data saturation – the point in the qualitative research data collection process when no new information is being discovered
• Focus groups- planned discussions designed to elicit group interaction and “obtain perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger & Casey, 2000, p. 5)
• Moderator- the researcher tasked with facilitating the conversation in the focus group

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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative study design: Qualitative study design

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• Observation
• Surveys & questionnaires
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Introduction

The effective evaluation of research involves assessing the way a study has been designed and conducted, and whether the method used was the most appropriate for answering the aims of the study. In contrast to quantitative studies, which are about breadth, qualitative research focuses on depth. 

Whereas quantitative research aims to develop objective theories by generating quantifiable numerical data, qualitative research aims to understand meaning. This might be the meanings that people attribute to their work, their behaviours or beliefs, or their attitudes or perceptions. Qualitative research is often based on methods of observation and enquiry; qualitative research “explores the meaning of human experiences and creates the possibilities of change through raised awareness and purposeful action” ( Taylor & Francis, 2013 ). Qualitative research focuses on life experiences; they are more about the “why” and “how” rather than the “how many”, or “how often”. 

Qualitative study designs might be chosen for any number of reasons. In health, you might be interested in finding out how nurses feel or experience care in the ICU; or you might want to find out how people engaged in heavy substance use found the experience of connecting with a support agency. Qualitative study designs are beneficial for certain types of research questions such as those looking to provide unique insights into specific contexts or social situations. However, they are not as strong when wanting to find direct cause and effect links or where a statistically significant result is required ( Taylor et al., 2006 ). 

Attribution and acknowledgement

Crediting creators and attributing content is a core part of both academic integrity and of being a digital citizen more broadly. This guide was created by Deakin Library.

The text and layout of Qualitative study design is © Deakin University 2023 and licensed under a CC BY NC 4.0

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

Need a helping hand?

Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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Types Of Qualitative Research Designs And Methods

Qualitative research design comes in many forms. Understanding what qualitative research is and the various methods that fall under its…

Qualitative research design comes in many forms. Understanding what qualitative research is and the various methods that fall under its umbrella can help determine which method or design to use. Various techniques can achieve results, depending on the subject of study.

Types of qualitative research to explore social behavior or understand interactions within specific contexts include interviews, focus groups, observations and surveys. These identify concepts and relationships that aren’t easily observed through quantitative methods. Figuring out what to explore through qualitative research is the first step in picking the right study design.

Let’s look at the most common types of qualitative methods.

What Is Qualitative Research Design?

Types of qualitative research designs, how are qualitative answers analyzed, qualitative research design in business.

There are several types of qualitative research. The term refers to in-depth, exploratory studies that discover what people think, how they behave and the reasons behind their behavior. The qualitative researcher believes that to best understand human behavior, they need to know the context in which people are acting and making decisions.

Let’s define some basic terms.

Qualitative Method

A group of techniques that allow the researcher to gather information from participants to learn about their experiences, behaviors or beliefs. The types of qualitative research methods used in a specific study should be chosen as dictated by the data being gathered. For instance, to study how employers rate the skills of the engineering students they hired, qualitative research would be appropriate.

Quantitative Method

A group of techniques that allows the researcher to gather information from participants to measure variables. The data is numerical in nature. For instance, quantitative research can be used to study how many engineering students enroll in an MBA program.

Research Design

A plan or outline of how the researcher will proceed with the proposed research project. This defines the sample, the scope of work, the goals and objectives. It may also lay out a hypothesis to be tested. Research design could also combine qualitative and quantitative techniques.

Both qualitative and quantitative research are significant. Depending on the subject and the goals of the study, researchers choose one or the other or a combination of the two. This is all part of the qualitative research design process.

Before we look at some different types of qualitative research, it’s important to note that there’s no one correct approach to qualitative research design. No matter what the type of study, it’s important to carefully consider the design to ensure the method is suitable to the research question. Here are the types of qualitative research methods to choose from:

Cluster Sampling

This technique involves selecting participants from specific locations or teams (clusters). A researcher may set out to observe, interview, or create a focus group with participants linked by location, organization or some other commonality. For example, the researcher might select the top five teams that produce an organization’s finest work. The same can be done by looking at locations (stores in a geographic region). The benefit of this design is that it’s efficient in collecting opinions from specific working groups or areas. However, this limits the sample size to only those people who work within the cluster.

Random Sampling

This design involves randomly assigning participants into groups based on a set of variables (location, gender, race, occupation). In this design, each participant is assigned an equal chance of being selected into a particular group. For example, if the researcher wants to study how students from different colleges differ from one another in terms of workplace habits and friendships, a random sample could be chosen from the student population at these colleges. The purpose of this design is to create a more even distribution of participants across all groups. The researcher will need to choose which groups to include in the study.

Focus Groups

A focus group is a small group that meets to discuss specific issues. Participants are usually recruited randomly, although sometimes they might be recruited because of personal relationships with each other or because they represent part of a certain demographic (age, location). Focus groups are one of the most popular styles of qualitative research because they allow for individual views and opinions to be shared without introducing bias. Researchers gather data through face-to-face conversation or recorded observation.

Observation

This technique involves observing the interaction patterns in a particular situation. Researchers collect data by closely watching the behaviors of others. This method can only be used in certain settings, such as in the workplace or homes.

An interview is an open-ended conversation between a researcher and a participant in which the researcher asks predetermined questions. Successful interviews require careful preparation to ensure that participants are able to give accurate answers. This method allows researchers to collect specific information about their research topic, and participants are more likely to be honest when telling their stories. However, there’s no way to control the number of unique answers, and certain participants may feel uncomfortable sharing their personal details with a stranger.

A survey is a questionnaire used to gather information from a pool of people to get a large sample of responses. This study design allows researchers to collect more data than they would with individual interviews and observations. Depending on the nature of the survey, it may also not require participants to disclose sensitive information or details. On the flip side, it’s time-consuming and may not yield the answers researchers were looking for. It’s also difficult to collect and analyze answers from larger groups.

A large study can combine several of these methods. For instance, it can involve a survey to better understand which kind of organic produce consumers are looking for. It may also include questions on the frequency of such purchases—a numerical data point—alongside their views on the legitimacy of the organic tag, which is an open-ended qualitative question.

Knowledge of the types of qualitative research designs will help you achieve the results you desire.

With quantitative research, analysis of results is fairly straightforward. But, the nature of qualitative research design is such that turning the information collected into usable data can be a challenge. To do this, researchers have to code the non-numerical data for comparison and analysis.

The researcher goes through all their notes and recordings and codes them using a predetermined scheme. Codes are created by ‘stripping out’ words or phrases that seem to answer the questions posed. The researcher will need to decide which categories to code for. Sometimes this process can be time-consuming and difficult to do during the first few passes through the data. So, it’s a good idea to start off by coding a small amount of the data and conducting a thematic analysis to get a better understanding of how to proceed.

The data collected must be organized and analyzed to answer the research questions. There are three approaches to analyzing the data: exploratory, confirmatory and descriptive.

Explanatory Data Analysis

This approach involves looking for relationships within the data to make sense of it. This design can be useful if the research question is ambiguous or open-ended. Exploratory analysis is very flexible and can be used in a number of settings. But, it generally looks at the relationship between variables while the researcher is working with the data.

Confirmatory Data Analysis

This design is used when there’s a hypothesis or theory to be tested. Confirmatory research seeks to test how well past findings apply to new observations by comparing them to statistical tests that quantify relationships between variables. It can also use prior research findings to predict new results.

Descriptive Data Analysis

In this design, the researcher will describe patterns that can be observed from the data. The researcher will take raw data and interpret it with an eye for patterns to formulate a theory that can eventually be tested with quantitative data. The qualitative design is ideal for exploring events that can’t be observed (such as people’s thoughts) or when a process is being evaluated.

With careful planning and insightful analysis, qualitative research is a versatile and useful tool in business, public policy and social studies. In the workplace, managers can use it to understand markets and consumers better or to study the health of an organization.

Businesses conduct qualitative research for many reasons. Harappa’s Thinking Critically course prepares professionals to use such data to understand their work better. Driven by experienced faculty with real-world experience, the course equips employees on a growth trajectory with frameworks and skills to use their reasoning abilities to build better arguments. It’s possible to build more effective teams. Find out how with Harappa.

Explore Harappa Diaries to learn more about topics such as What is Qualitative Research , Quantitative Vs Qualitative Research , Examples of Phenomenological Research and Tips For Studying Online to upgrade your knowledge and skills.

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”.

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.”

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection:

Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.

Criterion sampling-selection based on pre-identified factors.

Convenience sampling- selection based on availability.

Snowball sampling- the selection is by referral from other participants or people who know potential participants.

Extreme case sampling- targeted selection of rare cases.

Typical case sampling-selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo.

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research.

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others.

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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• Open access
• Published: 27 March 2024

Four antenatal care visits by four months of pregnancy and four vital tests for pregnant mothers: impact of a community-facility health systems strengthening intervention in Migori County, Kenya

• Yussif Alhassan 2 ,
• Lilian Otiso 1 ,
• Linet Okoth 1 ,
• Lois Murray 2 ,
• Charlotte Hemingway 2 ,
• Joseph M. Lewis 4 ,
• Mandela Oguche 1 ,
• Vicki Doyle 2 ,
• Nelly Muturi 3 ,
• Emily Ogwang 5 ,
• Hellen C. Barsosio 6 &
• Miriam Taegtmeyer 4 , 7  

BMC Pregnancy and Childbirth volume  24 , Article number:  224 ( 2024 ) Cite this article

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Metrics details

Early attendance at antenatal care (ANC), coupled with good-quality care, is essential for improving maternal and child health outcomes. However, achieving these outcomes in sub-Saharan Africa remains a challenge. This study examines the effects of a community-facility health system strengthening model (known as 4byFour) on early ANC attendance, testing for four conditions by four months of pregnancy, and four ANC clinic visits in Migori county, western Kenya.

We conducted a mixed methods quasi-experimental study with a before-after interventional design to assess the impact of the 4byFour model on ANC attendance. Data were collected between August 2019 and December 2020 from two ANC hospitals. Using quantitative data obtained from facility ANC registers, we analysed 707 baseline and 894 endline unique ANC numbers (attendances) based on negative binomial regression. Logistic regression models were used to determine the impact of patient factors on outcomes with Akaike Information Criterion (AIC) and likelihood ratio testing used to compare models. Regular facility stock checks were undertaken at the study sites to assess the availability of ANC profile tests. Analysis of the quantitative data was conducted in R v4.1.1 software. Additionally, qualitative in-depth interviews were conducted with 37 purposively sampled participants, including pregnant mothers, community health volunteers, facility staff, and senior county health officials to explore outcomes of the intervention. The interview data were audio-recorded, transcribed, and coded; and thematic analysis was conducted in NVivo.

There was a significant 26% increase in overall ANC uptake in both facilities following the intervention. Early ANC attendance improved for all age groups, including adolescents, from 22% (baseline) to 33% (endline, p  = 0.002). Logistic regression models predicting early booking were a better fit to data when patient factors were included (age, parity, and distance to clinic, p  = 0.004 on likelihood ratio testing), suggesting that patient factors were associated with early booking.The proportion of women receiving all four tests by four months increased to 3% (27/894), with haemoglobin and malaria testing rates rising to 8% and 4%, respectively. Despite statistical significance ( p  < 0.001), the rates of testing remained low. Testing uptake in ANC was hampered by frequent shortage of profile commodities not covered by buffer stock and low ANC attendance during the first trimester. Qualitative data highlighted how community health volunteer-enhanced health education improved understanding and motivated early ANC-seeking. Community pregnancy testing facilitated early detection and referral, particularly for adolescent mothers. Challenges to optimal ANC attendance included insufficient knowledge about the ideal timing for ANC initiation, financial constraints, and long distances to facilities.

The 4byFour model of community-facility health system strengthening has the potential to improve early uptake of ANC and testing in pregnancy. Sustained improvement in ANC attendance requires concerted efforts to improve care quality, consistent availability of ANC commodities, understand motivating factors, and addressing barriers to ANC. Research involving randomised control trials is needed to strengthen the evidence on the model’s effectiveness and inform potential scale up.

Peer Review reports

Attending at least four antenatal care (ANC) visits is essential for good maternal and child health outcomes, especially when accompanied by good quality of care [ 1 ]. Testing and early management of common antenatal conditions reduce the risks of maternal mortality and morbidity, stillbirth, low birthweight, pre-term delivery and HIV transmission [ 2 , 3 , 4 , 5 ]. The WHO 2016 ANC guidelines recommend starting care in the first trimester of pregnancy (12 weeks) for full ANC benefits, including HIV, anaemia, syphilis, malaria tests (in endemic zones), and supplements [ 6 ].

In Migori county, western Kenya, where this study was conducted, ANC attendance remains suboptimal despite high malaria endemicity and high HIV prevalence [ 7 ]. The county performed poorly compared to national standards in most maternal, newborn, and child health indicators. According to the 2022 Kenya Demographic and Health Survey [ 8 ], only 59% of women (aged 15–49) attended the recommended four ANC visits, even as the WHO now recommends eight ANC contacts for all pregnant women [ 6 ]; only 31% of women (aged 15–49) self-presented early enough (within the first 12 weeks of gestation) to fully benefit from testing and treatment for common pregnancy-related conditions. The delayed uptake of ANC, coupled with inconsistent availability of testing commodities limit the benefits for those who do attend, leading to delayed diagnoses of HIV, syphilis, anaemia, and malaria [ 9 , 10 , 11 , 12 ]. Teenage pregnancy is a concerning issue in the region, with 1 in 5 pregnant women being adolescents, who are less likely to seek timely ANC [ 8 ].

In sub-Saharan Africa, various factors contribute to delayed ANC uptake and failure to achieve the recommended number of visits, such as low knowledge of ANC benefits, stigma, financial constraints, fear of judgment/mistreatment, delayed pregnancy recognition, and limited access to quality ANC services [ 13 , 14 ]. A baseline assessment conducted at the dispensary level in Siaya county, western Kenya, revealed low testing rates for malaria and anaemia (27.8%), and moderate rates for syphilis (4.3%) among ANC attendees in 2017, while HIV testing rates were almost universal (99%). However, the subsequent integration of point-of-care testing and consistent supply of testing commodities in the same sites in 2018 significantly improved completion rates for all four tests to over 95%, and ensured appropriate management for those requiring treatment [ 11 ]. This increase was achieved without disrupting existing antenatal HIV testing services or impacting waiting times or staff workload; however, late presentation remained concerning [ 15 , 16 ].

Despite more women in sub-Saharan Africa now presenting for ANC at least once during pregnancy [ 6 , 17 , 18 ], interventions have been limited in improving early initiation, achieving four or more visits, and improving service quality. Mbuagbaw et al. [ 19 ] conducted a systematic review on the effects of health system and community interventions on ANC coverage. They identified various interventions used in low- and middle-income countries, such as financial incentives, mass media campaigns, community mobilisation, information-education‐communication, home visits by community health workers, behaviour change strategies, and policy change initiatives. However, only a few of these interventions effectively increased ANC coverage, with no single approach standing out. Since 2013, the Kenyan government has implemented free maternity policies to enhance maternal health service utilisation. Evidence indicates mixed effects of these initiatives on maternal health services, underscoring the need to combine such interventions with others addressing demand-side barriers to care and challenges in service delivery [ 20 ]. Community health volunteers (CHVs) with basic literacy and government-approved training play a crucial role in delivering maternal and child health services in Kenya by providing health promotion advice and referring pregnant mothers to ANC services during home visits [ 10 ]. Supporting CHVs in their role can lead to increased ANC uptake. For example, providing community health workers with free home pregnancy tests in a randomised controlled trial in Madagascar significantly improved pregnancy care by enabling early pregnancy confirmation and antenatal counselling [ 21 ]. Similar interventions employing quality improvement (QI) approaches at the community level in Kenya have improved skilled delivery and ANC attendance rates [ 22 , 23 , 24 ].

Our study aims to contribute to the discussion on effective interventions to improve the uptake and quality of ANC. This paper reports on a community health system strengthening model (called 4byFour) to increase ANC utilisation and quality. The model combines buffer stock supply and point-of-care testing for ANC, community pregnancy testing, and quality improvement strategies at the community-facility level to improve the quality and coverage of ANC. We assessed the feasibility and effects of the model on early ANC attendance, four ANC visits, and testing for four conditions by four months in Migori county, western Kenya.

Study design

We employed a mixed-methods quasi-experimental study with a before-after design, utilising unmatched quantitative analysis to assess the effect of the 4byFour model on the uptake of ANC and testing by four months of pregnancy, based on routine facility register data. Exploratory qualitative data was collected to enhance understanding of the findings. Our design was guided by process evaluation principles for complex interventions [ 25 , 26 ], adopting a concurrent approach for triangulation through simultaneous collection of quantitative and qualitative data [ 27 ].

Study setting and timeline

The 4ByFour model was co-developed and piloted with QI teams in two ANC facilities and their linked 6 community health units in Migori county. Migori is a predominantly rural county in western Kenya with 8 sub-counties and approximately 117 community units serving a population of about 1.1 million in 2019 [ 28 ]. The county was purposively selected on the basis of high maternal morbidity and low proportion of women attending ANC in the first trimester of pregnancy (21%) [ 17 , 29 ]; and due to well-established links with the County Health Management Team and previous experience with community QI approaches in the sub-counties. Suna West sub-county was purposefully chosen by the county team for the pilot project because it had experienced previous QI programs. Site selection criteria included a high patient flow; a larger, and at least one smaller, site; as well as a site with previous QI experience. The research team conducted a situational analysis using a standard checklist in the sub-county to identify suitable sites. Arombe and God Kwer met the criteria with four and two referring community units respectively; each saw 90–120 ANC attendances per month; and both had functional community-facility QI teams. God Kwer was more rural than Arombe which was on a major road. Baseline data were collected between August-December 2019; the intervention was implemented in a phased approach with interruptions as a result of COVID-19 lockdowns between March and June 2020; endline data was collected between August and December 2020.

Description of intervention: the 4byFour model

The 4byFour model was a community health QI approach designed to address gaps in both the demand and supply sides of the health system. The model name 4byFour describes its target of four tests (syphilis, anaemia, malaria and HIV) by four months (of pregnancy) and four (ANC) visits for all women [ 30 ]. The model was co-developed and piloted with QI teams in two ANC facilities and their linked six community health units in Migori county. Project resources were directed towards strengthening integrated point-of-care testing at the facility, community pregnancy testing and strengthening the community-facility linkage through community-facility quality work improvement teams (WITs). Traditional facility-based QI approaches were adapted to the community level to ensure they were simple, jargon-free and could be understood and implemented by integrated teams of community health volunteers and health facility staff. This adapting of QI has been suggested to be the missing piece in QI efforts in LMICs [ 31 ]. Community-facility work improvement teams brought together community health volunteers (CHVs); community members; community health assistants (CHAs), who serve as supervisors of CHVs; ANC nurse staff and the facility-in-charge of the link primary care facility. The WITS reviewed data collected at community and facility level monthly, analysed it and used it to prioritise, implement and review appropriate interventions to improve ANC attendance during the intervention. CHVs and their supervisors were trained in pregnancy mapping and the distribution and interpretation of simple urine pregnancy tests at community level [ 21 ]. During the intervention period (Feb - Oct 2020), we provided buffer stocks of rapid diagnostic test kits to the study facilities to enhance their testing capacity and avoid shortages, without disrupting the county government and KEMSA’s supply system. These facilities were equipped with HemoCue machines for haemoglobin measurement, rapid diagnostic test kits for malaria (SD Bioline Malaria Ag p.f/Pan test), and HIV/Syphilis test. Buffer stocks were provided only in the case of stock outs identified through our monthly commodity checks. Laboratory and ANC staff came up with an agreed approach to ensure testing at the point-of-care during the ANC consultation to improve availability and reduce waiting time and to record results accurately in both laboratory and ANC registers. Standard practice was to record only positive malaria results in the ANC paper register and training was given to record both positive and negative malaria tests in a spare column of the register. Supportive supervision was carried out by the sub-county health management team members quarterly to review implementation, data quality and other gaps. The research implementation team provided monthly coaching and mentorship to the WITs.

Study populations and sample size

We included all sequential ANC attendances at the two facilities in our quantitative analysis. Using the Migori estimate prevalence of 21% of women attending ANC prior to 4 months [ 17 ] a significance level of 5% and a power of 80%, we needed to review at least 252 women’s data at baseline and endline to detect at least a 50% relative increase in the uptake of early ANC visits and testing.

Participants for the qualitative study included those directly involved as deliverers and/or beneficiaries of care i.e., pregnant mothers, community health volunteers (CHVs) and their supervisors, the Community Health Assistants (CHAs), facility staff, and senior officials of the Migori County Health Management Team. Pregnant mothers and facility staff were purposively selected from facilities where the quantitative data was abstracted, and sampled based on their experience of the intervention, willingness to participate and ability to provide consent. The CHAs, CHVs were linked to the study facilities and operated within the community health units of the facilities. The pregnant mothers were purposively sampled to represent adolescents (< 19 years) and older adults. They were approached in-person by the researchers as they visited the facility to access ANC or directly in the community. The county health officials, facility staff, and CHV/CHAs were invited (mostly by phone or in-person) to the study based on their role and interviewed if they consented. Sample size was determined by data saturation, deemed to have been reached when no new themes emerged from additional interviews [ 32 ].

Data collection and management

Quantitative.

Baseline data were collected from August to December 2019 and endline data collected during the same period in 2020. Data collection was impacted by interruption in intervention implementation by COVID-19 lockdown. As part of routine data collection, each ANC attendee was assigned an ANC number by the healthcare worker who completed the register. The numbers were assigned sequentially to women on their first ANC visit, considering the number of women in attendance, and the month and year of their ANC visit. ANC numbers did not follow any conventions to guarantee uniqueness. Data on ANC attendance, ANC testing, age and parity were extracted from the paper-based routine ANC registers to Microsoft Excel by a research assistant. Electronic data sets were then reviewed by facility staff from both sites until agreement was reached on the accuracy of the data. To extract data on distance to facility, we consulted the CHVs to assign a distance in kilometres to each of the village names in the visitation records. Data were double checked for accuracy. We compared clinical details (parity, age and village name) for each ANC number. For ANC numbers with different clinical details, we reviewed original paper records to make a judgement on whether the clinical details differed and ANC numbers with different clinical details were excluded from the analysis, as were records with blank or ambiguous ANC numbers.

Qualitative

Data were collected through individual interviews to explore the issues in greater depth and enable participants to speak openly [ 33 ]. We conducted in-depth interviews IDIs with pregnant mothers, CHVs, and facility staff at local health facilities, and key informant interviews with senior county health officials at county health offices. The interviews were carried out between November and December 2020 by experienced qualitative researchers with knowledge of the local language, culture and health system. They were conducted face-to-face and in English or Luo; lasted for about 1 h; were audio recorded and complemented with written notes. Semi-structured topic guides were used to inform the interviews; they were piloted and revised iteratively as data collection evolved. Interviews explored issues about ANC attendance, data quality, QI interventions and participants’ perception of the effects and challenges of the 4byfour intervention.

Data analysis

Statistical analysis.

Analysis was conducted in R v4.1.1 [ 34 ]. Descriptive statistics are medians with interquartile ranges or proportions with exact binomial confidence intervals as appropriate. Difference in patient characteristics between baseline and endline was assessed with Fisher’s exact test (categorical variables) or Kruskal-Wallace test (continuous variables). Negative binomial regression was used to test the hypothesis that the number of unique attendees increased from baseline to endline. Regression models were fitted to the number of weekly new attendees separately for the two clinics. We assessed the proportion of pregnant women who had first ANC visit before 16 weeks gestation; who had all four tests before 16 weeks gestation and who had 4 ANC visits before 36 weeks gestation. Logistic regression modelling was used to correct for the following a priori selected covariates: study period, clinic, age, parity and distance to clinic. We modelled the impact of patient factors on outcomes. A model including study period (baseline or endline) and clinic only as a covariate for each outcome was compared to a model including study period, clinic and all patient covariates described above (age, parity and distance to clinic) using likelihood ratio testing and the Akaike Information Criterion (AIC). A p-value < 0.05 and a lower AIC for the model including patient factors was interpreted as meaning patient factors explain some variability in outcome. Analysis of receipt of four tests was restricted to endline participants (because no participant at baseline received all four tests), and the study period variable was not included.

Qualitative analysis

Interviews were transcribed using a denaturalised approach and checked for accuracy and completeness [ 35 ]. The Luo interviews were translated into English. Data was analysed in Nvivo12 based on thematic framework approach. We first developed a coding framework based on a review of a sample of the transcripts, which was piloted and revised. Using the coding framework each transcript was systematically analysed to identify relevant codes, categories, and themes. An initial analysis of the quantitative data enabled the analysis to capture relevant qualitative data needed to triangulate emerging quantitative findings, including the perceived reasons for the increase in early ANC attendance, access to ANC test, and barriers to uptake of 4 ANC visits. Emerging findings were discussed among authors, feedback was obtained and subsequently integrated into the analysis.

There were 787 unique ANC numbers at baseline and 949 at endline. Among these, 80 baseline and 55 endline ANC numbers were excluded because they included participants with the same numbers but with different clinical details. This resulted in 707 baseline and 894 endline participants included in the analysis. Table  1 presents the case mix at baseline for the two clinics. Arombe had a younger age profile, but the median parity [ 1 ] and gravidae [ 2 ] were the same at both clinics, with more multiparous women attending Godkwer. Most women booked their first visit after 27 weeks gestation, and this was more common in Arombe. A minority of women (28%) attended four or more visits, and this pattern was similar at both clinics.

Early ANC attendance

There was a statistically significant 26% increase in overall uptake of ANC across both clinics (Arombe 369 to 494 attendees IRR 1.5 [95% CI 1.1-2.0, p  = 0.008], Godkwer 338 to 400 IRR 1.3 [95% CI 1.0-1.7, p  = 0.048]) with more women attending for first visit before 16 weeks’ gestation: 22% (79/359) at baseline compared to 33% (119/365) at endline ( p  = 0.002) (Table  2 ). This increase was seen across all age groups including adolescents: 18% (21/109) of adolescents attended before 16 weeks at baseline and 32% (32/99) at endline ( p  = 0.025) (Table  2 ).

The increase remained after correcting for changing case mix from baseline to endline in a logistic regression model as shown in Table  3 (aOR 1.69 [95% CI 1.11–2.50], p  = 0.015). The logistic regression models including patient factors (age, parity and distance) were a better fit to the data (AIC 530.2 for patient-factor model vs. 537.8, p  = 0.004 on likelihood ratio testing) suggesting patient factors are associated with early booking, despite the fact that the confidence intervals of the estimates of odds ratios crossed 1.

A total of 37 participants took part in the qualitative interviews. The qualitative data suggested an improved understanding of the benefits of early ANC among women after CHV visit, resulting in enhanced motivation to present early for ANC. Pregnant women reported receiving ANC education from CHVs, and many demonstrated awareness of the benefits of early ANC. Participants reported increased early detection and referral of pregnant mothers due to the community pregnancy testing, resulting in early ANC initiation: “ previously, we could only refer obvious pregnant mothers, when the pregnancy is showing, about 30 weeks gestation…. Now we can identify them early and encourage them to start early. The [pregnancy] kits have really helped (CHV, Arombe). Several women said they were encouraged to attend ANC if a referral was backed by a positive pregnancy test: “You feel it is urgent [to attend ANC] if the CHV tests and finds that you are positive.” (Pregnant mother, < 18 years, Arombe). CHVs noted younger women, especially primigravida, were more receptive to the message of early ANC attendance compared with older women with previous pregnancy experience. The former appeared to be motivated by ANC testing and the need to keep their baby safe; they perceived a greater sense of insecurity and were more easily persuaded to visit ANC as a way of mitigating these risks. The latter felt they were experienced at pregnancy and childbirth. Some perceived the ANC test and iron supplements were not necessary since they had had them in their previous pregnancy.

“ The young women are eager to go; if you tell them they start clinic. But the older women feel like they can even give birth at home by themselves” (CHV, Masara).

While women were aware of the benefits of ANC attendance some did not know the ideal gestational time for first ANC visit and the benefits of early attendance. Many still believed ANC attendance was only needed when they were ill or had experienced health challenges in their previous pregnancy: “ Coming early depends on how you are feeling and might feel that you need to go to the clinic. …you are not feeling sick or anything therefore you feel there is no need to start early”. (Pregnant mother, 18 + years, GodKwer). Women presented late to avoid having to make many follow-on visits due to financial constraints and distance.

“Now that we have the kits, if you confirm her pregnancy at an early stage, they fear coming to the facility because they are required to attend clinics until delivery… some stay very far away from the facility like myself who uses fifty shillings for transport, they deem that as costly if started at an earlier stage.” (CHV, Masara).

Availability of ANC profile tests

The project’s buffer stock improved the erratic ANC test profile supply from the national system. From February to October 2020, the project supplied more HB cuvettes, HIV/Syp DUO Kits, and Rapid Syphilis Kits than the national system (Table  4 ). The project supplied fewer mRDTs, causing stockouts of 41 and 53 days in Arombe and Godkwer, respectively prior to Buffer stock distributions. A 20-day stockout of HB cuvettes occurred mainly in Arombe, while Godkwer had none partly due to the project’s buffer stock. The national system did not supply any Rapid Syphilis Kits, leaving the project as the only source of 100 kits; both facilities faced 120 days of stockout for this commodity (Table  4 ).

Four tests by four months

At baseline no women had received all four tests by four months (16 weeks) (Table  5 ). Following the intervention and supply of buffer stocks this had increased to 3% (29/894). The proportion of women receiving haemoglobin and malaria testing increased to 8% and 4% respectively. These were significant increases ( p  < 0.001) but remained low due to insufficient profile tests not covered by the buffer.

There was an overall increase in women testing driven by the increased malaria and haemoglobin testing (Fig. 1).

Proportion of participants receiving ANC tests at any gestation stratified by clinic

We carried out a post-hoc analysis of receipt of 4 tests at any gestation and showed the same pattern. No women at any gestation were recorded as having received all 4 tests at baseline and 148/894 (17%) women were recorded as receiving 4 tests at any gestation at endline. Providing enough buffer stock could have boosted test uptake significantly. Patient factors of age, parity and distance from clinic were not associated with testing (AIC for patient-factor logistic regression model 417.8 vs. 415.3, p  = 0.315 on likelihood ratio testing, with odd ratios of effect size crossing 1 as before) (Table  6 ).

Our qualitative interviews revealed the importance of a reliable supply of ANC commodities. Stockout of ANC profile commodities not covered by our buffer stock was widely reported and attributed to erratic supply by the County government. Apart from the HIV/Syphilis duo kit, the malaria RDT, syphilis rapid tests, and HemoCue cuvettes which had been out of stock for periods ranging from 2 to over 6 months when checked.

“ What has not worked well for me is the supply of ANC kits.… there is no regular supply of these kits from the County government and there is nothing you can do about it. At least when there is 4byFour program going around I will not have some of this problem challenge. I wish the County government will take charge and learn from what 4byFour is doing (Facility staff, Arombe).

Integrated point-of-care testing was hampered by inadequate space to administer the test outside of a laboratory. Many MCH units were too small and lacked the privacy to carry out some of the tests at consultation, such as HIV and syphilis: “ Testing at the point-of-care is a good idea but the challenge for us is the space and lack of privacy” (Facility staff, GodKwer). Further, respondents reported limited availability of MCH and laboratory staff and training on point-of-care testing, leading to delays in testing turnaround time. Other concerns related to regular power blackouts with no backup which meant laboratory tests could not be conducted.

Monthly physical checks of stock for tests and recommended treatments for each condition revealed consistent supplies for HIV testing only, with inconsistent supply from the county stores of syphilis (of HIV/syphilis duo), malaria rapid tests and the absence of cuvettes for point-of-care haemoglobin tests (using HemoCue). Drug stockouts were common. While antiretrovirals were consistently available, simple treatments including iron and folate were often unavailable.

Four or more ANC visits

Our 4byFour model did not impact the proportion attending 4 ANC visits in pregnancy among those who would reach 36 weeks gestation during the study period (Table  7 ) and we found no association of 4 or more visits with patient factors (AIC 765.2 for patient factors model vs. 764.4, p  = 0.156).

Some providers perceived an increase in women making fourth or more ANC visits, attributed to starting ANC earlier. Other reasons included increased CHVs monitoring (and nudging) pregnant mothers and potential increased awareness of ANC benefits.

“ The uptake [of first ANC] has increased but the other… the 4th, 5th and 6th ANCs those have not been coming so much. Like when a mother has come for even 3 ANCs then they are not bothered to come for the next ones….” (CHA, GodKwer). “When they start in the first month, they get many appointments, so they are able to go many times before their delivery time is due. … we visit them and remind them.” (CHV, Arombe).

Interview data suggests women’s motivation decreased once they have finished taking the scheduled test and drugs in earlier visits. Women perceived no need to visit once the tests/supplements have been completed, especially if no health issues have been diagnosed. Additional factors included distance to the facility and lack of money for transport:

“… they also say that “when I go there, I am going to wait for so long and after all I have gone 3 times and I didn’t have complications, I have taken IFAS and I am fine” (Pregnant mother, 18 + years, GodKwer). “ It is too far, and I can’t be going every month. If I go first, second and then wait closer to delivery I go again…. I have no money to travel there all the time ” (Pregnant mother, < 18 years, Masara).

This study assessed the feasibility and effects of the 4byFour model on early ANC attendance, four ANC visits, and four ANC tests by four months in Migori county. The model integrated existing health system models and offers a unique methodology for applying them in real life settings, advancing from ‘improvement science’ to ‘implementation science’. We found the community components of the intervention, involving pregnancy mapping, enhanced health education and referral by CHVs, significantly increased early ANC attendance among women of all ages, including adolescents. The facility-level intervention, involving buffer stock supply and point-of-care testing, increased testing overall but only marginally for women receiving four ANC tests by four months as this was determined by early attendance. The model had no effect on the proportion of women attending four or more ANC visits. The study did not yield sufficient evidence to evaluate the contribution of community-facility work improvement teams on QI and ANC uptake.

The improvement in early ANC attendance associated with community pregnancy testing and enhanced counselling and referral of pregnant mothers by CHVs is consistent with other studies showing CHW interventions increase ANC attendance [ 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Similar to the findings of Comfort et al. in Madagascar [ 43 ], our study demonstrates that CHVs’ distribution of pregnancy tests not only improves early detection and referral for initiating ANC at facilities but also appeared to enhance the reputation and credibility of CHVs as primary care providers. Examining these secondary effects on CHWs and their roles, as well as the socio-cultural effects on clients and communities in future research, will enrich understanding of community-based pregnancy testing. Our data also reveals the limitations of solely increasing pregnancy testing access and acknowledges other barriers to ANC utilisation that require attention. Important demand side factors such as age, parity and distance affected early ANC attendance despite the interventions, as older, multi-parous women often did not see the need to present early for ANC having gone through previous pregnancies successfully and were conscious of costs and time involved in ANC visits. Similar to findings in Uganda [ 44 ] and Rwanda [ 45 ], a multi-country study in Ghana, Kenya and Malawi found parity and age had complex impacts on ANC initiation [ 46 ]. Primigravidae were more likely to seek care early once aware of their pregnancy but less likely to recognise early pregnancy [ 46 ]. Similar to our findings, several studies have found adolescents and unmarried women delay ANC attendance due to stigma, unwanted or unplanned pregnancy or the desire to terminate the pregnancy [ 47 ]. In some communities, superstitious beliefs limit women reporting for early ANC as they do not want to disclose pregnancy status before 12 weeks for fear of pregnancy loss or curse/witchcraft [ 47 , 48 , 49 ]. This indicates the importance of a sensitive approach by community health workers with community pregnancy testing, counselling and referral. Implementing pregnancy testing initiatives alongside efforts to address other demand and supply-side barriers is crucial for maximum impact.

The model’s failure to improve attendance for four or more ANC visits suggests that solely increasing early ANC initiation, while proven to enhance the odds of having four ANC visits in certain cases [ 50 ], is insufficient to ensure consistent or four ANC visit attendance. Accessibility challenges, such as distances to facilities and financial constraints, were widely reported to affect subsequent ANC visits after initiation and aligns with findings across LMIC contexts [ 44 , 45 ]. Behavioural factors, such as women’s limited understanding of the preventive value of ANC and the benefits of follow-on attendance (beyond the first ANC visit), were equally pertinent. Cultural, spiritual beliefs, personal issues, and variable ANC service quality in health facilities can impact ANC attendance [ 51 , 52 ]. Quality of care factors, including infrastructure, commodities, supplies, and health worker skills and attitudes, affect ANC visits in most LMIC contexts [ 53 ]. Inequalities in care quality have been noted in certain settings, indicating their potential impact on disparities in ANC attendance. A study in Kenya found the youngest, poorest, least educated, most disadvantaged, and most disempowered women are most likely to report poor experiences of care [ 54 ]. This suggests sustained patient-centred QI efforts are needed to address health inequalities and improve ANC attendance. While Kenya’s Linda Mama initiative offers free maternal and child health services, coverage is incomplete, and it does not cover transportation costs [ 55 ]. Decentralising ANC by training community health workers to provide low-risk antenatal care at the community level, such as distribution of IFAS and IPTp, and pregnancy testing, could reduce the distance barriers [ 41 ].

The low uptake of four test by four months partly results from poor ANC attendance in the first trimester, when most tests were done as per national guidelines. Additionally, we observed major procurement and supply chain issues for anaemia testing, malaria rapid tests and iron/folate supplements, which may have hindered the model’s impact on early and 4 ANC visits. County stockouts prevented four tests from being done, which discouraged women from attending subsequent ANC visits. Even when test commodities were available, other factors such as human resource shortages, lab testing, and inconsistent recording of malaria results limited the effect of the increased commodity availability for test uptake. Lab tests were affected by power blackouts, while point-of-care tests were affected by lack of privacy and confidentiality. HIV testing and antiretroviral therapy were consistently high and unaffected by the intervention, indicating their support from vertical programmes compared to other ANC elements. Stockouts of essential commodities are a significant challenge in ANC and highlight the fragmentation of supply systems along vertical disease programmes [ 56 ]. Several studies have reported that commodity stockouts discourage pregnant women from attending ANC in Africa [ 57 , 58 , 59 ], although there is limited evidence on the effects of buffer stock interventions on ANC attendance. Nonetheless, our provision of buffer stock for essential commodities improved ANC test uptake and quality care by smoothing out stock issues, demonstrating the critical importance of sustained commodity availability in ANC utilisation beyond donor funded projects and research. Buffer stock alone could have produced similar intervention outcomes. Thus, effective ANC requires integrated supply chains to ensure availability of core primary care essential commodities [ 60 , 61 ]. Core treatment for common conditions such as anaemia or malaria may be overlooked by top-down programmes from large multilateral organisations, as seen in studies in Tanzania, Zambia and elsewhere where the well-funded HIV program reduced ANC clinic attendance and testing of other conditions [ 50 , 62 , 63 ].

Similar to prior findings [ 64 ], our study identified significant data quality issues, including incomplete and inaccurate ANC registers, a lack of unique patient identification for tracking, data fragmentation among registers, and disconnected health data between community and facility levels. Digitised approaches to data collection at both community and facility levels could potentially address these challenges, but long-term sustainability beyond project funding is imperative [ 65 ]. The Kenyan MoH has recognised the potential of digitised health data to tackle data quality concerns, culminating in the launch of a costed strategy to guide a fully national electronic Community Health Information System (eCHIS), piloted in Kisumu County [ 66 ] and now being rolled out across the country. Establishing community-based ANC to complement facility-based digital ANC records and creating sustainable linkage between these platforms are essential steps to help Kenya achieve WHO’s ambitious goal of eight ANC contacts.

The QI approach of the 4byFour pilot was shown to work to improve CHV pregnancy testing, referral and linkage to health facilities (demand side). The intervention was based on a health system strengthening approach and focused on improving existing systems and resources to optimise ANC service delivery, rather than introduce new elements. During the 4byFour pilot, the local implementing partner, research team, county health team, community health volunteers and facilities worked together to co-design the intervention aiming to work within and maximise the existing capacity of the system to promote sustained quality improvement. However, it faced numerous sustainability challenges of testing procurement and supply chain, workforce capacity, and intersecting vertical programs demonstrating the need to effectively address both supply and demand side factors to effectively achieve ANC outcomes. Sustainability of QI interventions beyond project funding is essential to strengthen health systems and deliver lasting improvement in maternal and child health outcomes [ 67 ].

Strengths, limitations, and future research

This study offers valuable insights into the potential effects of combining various health system strengthening approaches on antenatal care attendance, while providing useful insights on the individual components of the model. However, it has some limitations. The before-after design limits our ability to rule out other factors that may have caused the observed changes from baseline to endline. Data quality issues from paper-based ANC registers extraction may compromise data reliability, despite data review by facility staff. Budget constraints hindered the buffer stock intervention from addressing all essential commodity stockouts, possibly affecting the model’s effectiveness. The cross-sectional design limits causal inferences from participants’ experiences. Future research using longitudinal and randomized controlled trials will enhance the evidence on the model’s impact. Moreover, a cost-effectiveness analysis and an examination of contextual factors influencing the model’s outcomes will be useful in informing future scale-up efforts. There is the need for innovative approaches to assess the potential effect of the QI component of the model on ANC uptake.

This study demonstrates the potential of the 4byFour model to improve ANC coverage in resource-poor health systems. The model increased ANC uptake, especially early ANC attendance among all age groups, including adolescents who usually engage less in care during pregnancy. The model also improved essential ANC testing for malaria, HIV, syphilis, and haemoglobin. Community pregnancy testing and buffer stock provision of ANC profile tests had particularly promising results. The findings suggest that the 4byFour model and its components, such as community pregnancy testing and buffer stock provision of ANC commodities, can be used to tackle low and delayed ANC uptake and quality issues. Sustained improvement in ANC attendance requires a concerted effort to improve quality of care and availability of ANC commodities, understand motivating factors and barriers to ANC, and promote incentives for horizontal investment in health system strengthening that prioritises integrated patient-centred care over fragmented verticalisation. Further research using longitudinal and randomised control trials is needed to strengthen the evidence on the model’s effectiveness and scale up.

Data availability

Data are available from the corresponding author on request.

Abbreviations

Antenatal Care

Community Health Assistants

Community Health Volunteers (now known as Community Health Promoters)

Human Immunodeficiency Virus

Low-and Middle-Income Countries

• Quality improvement

World Health Organization

Work Improvement Teams

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Acknowledgements

The authors express their gratitude to all the study participants. We are also grateful to the Migori County Health Management Team, particularly to Boniface Olalo, for facilitating lab training of CHVs. The work would not have been possible without the staff of Arombe and God Kwer Health Centres; and the able research assistants. We are particularly grateful to Mr Jared Odaro whose extra support to the data collection and learning event were much appreciated.

This project was funded through an MRC Public Health Intervention Development (PHIND) award.

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All authors contributed to the study. LO, MT, LM, YA, LO, MO, NM, CH and VD conceptualised and designed the study; LO, MT, LM and YA conducted literature review. YA, LM, MO, NM and JL supervised data collection and analysed the data which were interpreted by MT, LO, EO, VD, JL, CH, NM, MO, LO, YA LM and LO. YA, LO, LM, MT and JL drafted the manuscript, and all authors critically reviewed the draft. All authors read and approved the final manuscript.

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Alhassan, Y., Otiso, L., Okoth, L. et al. Four antenatal care visits by four months of pregnancy and four vital tests for pregnant mothers: impact of a community-facility health systems strengthening intervention in Migori County, Kenya. BMC Pregnancy Childbirth 24 , 224 (2024). https://doi.org/10.1186/s12884-024-06386-2

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Evaluation of heroin-assisted treatment in Norway: protocol for a mixed methods study

• Lars Henrik Myklebust 1 ,
• Desiree Eide 1 ,
• Espen A. Arnevik 4 ,
• Omid Dadras 2 ,
• Silvana De Pirro 1 , 6 ,
• Rune Ellefsen 4 ,
• Lars T. Fadnes 2 , 3 ,
• Morten Hesse 5 ,
• Timo L. Kvamme 5 ,
• Francesca Melis 1 ,
• Ann Oldervoll 1 ,
• Birgitte Thylstrup 5 ,
• Linda E.C. Wusthoff 1 , 4 &
• Thomas Clausen 1  

BMC Health Services Research volume  24 , Article number:  398 ( 2024 ) Cite this article

Metrics details

Opioid agonist treatment (OAT) for patients with opioid use disorder (OUD) has a convincing evidence base, although variable retention rates suggest that it may not be beneficial for all. One of the options to include more patients is the introduction of heroin-assisted treatment (HAT), which involves the prescribing of pharmaceutical heroin in a clinical supervised setting. Clinical trials suggest that HAT positively affects illicit drug use, criminal behavior, quality of life, and health. The results are less clear for longer-term outcomes such as mortality, level of function and social integration. This protocol describes a longitudinal evaluation of the introduction of HAT into the OAT services in Norway over a 5-year period. The main aim of the project is to study the individual, organizational and societal effects of implementing HAT in the specialized healthcare services for OUD.

The project adopts a multidisciplinary approach, where the primary cohort for analysis will consist of approximately 250 patients in Norway, observed during the period of 2022–2026. Cohorts for comparative analysis will include all HAT-patients in Denmark from 2010 to 2022 ( N  = 500) and all Norwegian patients in conventional OAT ( N  = 8300). Data comes from individual in-depth and semi-structured interviews, self-report questionnaires, clinical records, and national registries, collected at several time points throughout patients’ courses of treatment. Qualitative analyses will use a flexible inductive thematic approach. Quantitative analyses will employ a wide array of methods including bi-variate parametric and non-parametric tests, and various forms of multivariate modeling.

The project’s primary strength lies in its comprehensive and longitudinal approach. It has the potential to reveal new insights on whether pharmaceutical heroin should be an integral part of integrated conventional OAT services to individually tailor treatments for patients with OUD. This could affect considerations about drug treatment even beyond HAT-specific topics, where an expanded understanding of why some do not succeed with conventional OAT will strengthen the knowledge base for drug treatment in general. Results will be disseminated to the scientific community, clinicians, and policy makers.

Trial registration

The study was approved by the Norwegian Regional Committee for Medical and Health Research Ethics (REK), ref.nr.:195733.

Peer Review reports

Opioid use disorder (OUD) is a major global health concern with an estimated caseload of 31.5 million in 2022 [ 1 ]. It is frequently related to infectious diseases from injection-based drug use, psychiatric disorders, deterioration of social relations, reduced workforce participation, and a tenfold increase in crude all-cause rate of mortality [ 2 ]. The treatment and care for patients with OUD has gradually developed from an initial emphasis on abstinence and withdrawal management, to regular prescriptions of opioid agonists for maintenance treatment (OAT) [ 3 ].

Half a century after the first initiatives of prescribing methadone for OUD in a regular manner [ 4 , 5 ] OAT now has a strong evidence-base [ 6 ]. Overall, it contributes to a substantial reduction in mortality, general health benefits, and reduced use of illicit drugs and criminal activity [ 6 , 7 , 8 , 9 ]. Still, not all individuals find conventional OAT sufficiently attractive over time, and cycles of dropout and re-entering are ongoing challenges in these programs [ 10 , 11 , 12 ]. A variable retention rate of 20–84% has been observed [ 13 ]. Among the efforts to improve the inclusion of patients in OAT is the introduction of more diverse medication options, such as rapid-onset, short-acting injectable pharmaceutical opioids such as heroin [ 14 ].

The use of medical grade heroin (diacetylmorphine) in treating OUD has been applied in England since the 1920s, originally as hand-out prescriptions to take home [ 15 , 16 ]. Initiatives to incorporate it into more regular OAT started in Switzerland in 1994, with promising results [ 17 , 18 ]. Now, three decades later and after clinical trials from several European countries and Canada, the body of research suggests that heroin-assisted treatment (HAT) is beneficial for a sub-selection of patients in regard to health outcomes and reductions in use of illicit drugs and criminal behavior [ 19 , 20 , 21 ]. The results are less clear for longer-term outcomes such as mortality [ 6 , 19 ].

Still, HAT remains politically controversial [ 22 ], and reduced illicit heroin use and criminal behavior may not be compelling arguments for its efficacy. Rather, as for any other medical treatment its impact may better be assessed by patients’ improvement in quality of life, everyday level of function, and mortality [ 23 ].

Although newer studies suggests that take home doses are a feasible and safe alternative for patients deemed suitable [ 24 , 25 ], medical heroin is typically administered under rigorous and comprehensive medical supervision due to the risk of serious adverse events and diversion [ 26 ]. Studies on cost effectiveness suggest both excessive expenses and inconclusive results when compared with methadone treatment, which are possibly dependent on methodological issues and poor consideration of the mechanisms involved [ 20 , 27 , 28 ].

Additionally, most of the research on the effectiveness of HAT originates from randomized clinical trials which may have limitations concerning the understanding of long-term outcomes and the mechanisms behind [ 23 ]. Thus, the main contribution of HAT may lie in the engagement of a high-risk population in utilization of health- and social services over time, like the more conventional options of OAT [ 23 , 29 ]. A more comprehensive view of outcomes beyond the mere quantity and frequency of drug use and criminal behaviour can provide crucial information about the mechanisms responsible for treatment effectiveness, and its possible impact on other clinically and socially relevant parameters [ 30 ].

The current Norwegian HAT study is presented in this context. The study is part of a clinical project by the Norwegian Directorate of Health, with the aim to evaluate the implementation of HAT into the national OAT services. It is based on a model from Denmark where the use of medical heroin was introduced in 2010, following the British “RIOTT” line of test trials from 2005 [ 31 ]. Denmark currently has five clinics as permanent parts of the national healthcare system, although a limited amount of research has been published from this model [ 32 ].

The Norwegian HAT-project

OAT programs based on prescription of methadone and buprenorphine has in various forms been integrated into the Norwegian health and social services-system since 1997 [ 33 ]. In the spring of 2020, the Norwegian Directorate of Health introduced a time-limited, clinically based project on the use of pharmaceutical heroin in the specialist healthcare services. Based on a day-center model, treatment is offered at two designated clinics in the largest Norwegian cities of Oslo and Bergen. The clinics consist of injection sites and medical personnel for the administration of pharmaceutical heroin twice a day, in combination with a take-home oral overnight dose of slow-releasing opioid-agonist such as methadone or morphine. Take-home doses of heroin are not granted, and patients must attend daily all year around. Psychosocial services and support are also offered [ 34 ]. Patients are referred from other services of substance use disorder treatment, specialist healthcare services or general practitioners. Criteria for admission are ongoing OUD with at least one former attempt of conventional OAT, being over 18 years of age and with general competency of consent. Exclusion criteria are severe mental disorders with reduced competency of consent, pregnancy, or repeated violent behavior.

The Norwegian Centre for Addiction Research (SERAF) at the University of Oslo was granted the research-based evaluation of the HAT project in 2021. The study will be conducted together with Section for Clinical Addiction Research (RusForsk) at Oslo University Hospital, Bergen Addiction Research Group (BAR) at Haukeland University Hospital in Bergen, Centre for Alcohol and Drug Research (CRF) at Aarhus University in Denmark, and the Norwegian user organization proLARNett.

The primary aim of the research project is to examine the effects from implementing HAT in Norway for individual patients and for the health services organization. A secondary aim is to compare these findings with the Danish HAT program.

Based on the Norwegian Directorate of Health’s specifications in the project proposal, the study will cover the following thematic areas:

Explore the attitudes, experiences and challenges of HAT as perceived by patients, their relatives, and clinical staff.

Describe changes in mental and physical health among patients receiving HAT, and in what way it is associated with outcomes such as quality of life, utilization of health- and social services, social reintegration, criminal behavior and use of illicit drugs.

Report any serious adverse events and incidents at treatment initiation, during treatment, and after discharge from HAT.

Perform an economic evaluation of the program with associated clinical benefits and societal costs.

Evaluate the organizational processes involved in the implementation of HAT in Norwegian specialist healthcare services, and the eventual impact from HAT on OUD patients’ utilization of conventional OAT.

Additional research relevant to HAT that is not explicitly outlined in the proposal (may require additional approvals from the Norwegian Regional Committee for Medical and Health Research Ethics.)

The themes were operationalized into six work packages, with corresponding research questions and data sources (shown in Table  1 ).

Methods and design

The project is a multi-dimensional study, involving an array of methodological approaches and data sources. The main part is a prospective cohort study of all Norwegian HAT patients, compared with the cohorts of all Danish HAT patients and Norwegian patients in conventional OAT.

Study populations and size

The primary target group is all patients enrolled in the two HAT clinics in Oslo and Bergen during the period 2022–2026, with an expected total sample size of N  = 250. Based on earlier findings, the ratio of men to women is expected to be 4:1, with an age distribution of 27–60, presenting multiple substance use disorders. As the study is based on the total clinical population, representation will be determined by its demographics, with no exclusion of genders or ethnic minorities. The patients who have applied to but have not been accepted for HAT will be used for comparison, with an expected sample size of 100.

Comparative data from the Danish cohort will be drawn from the comprehensive dataset at Aarhus University from 2010 and onwards, with a sample size of approximately 500 [ 35 ]. Likewise, the comprehensive dataset at SERAF on the cohort of Norwegian patients in conventional OAT from 2003 has an approximate sample size of 8300.

Data sources

Data on the primary cohort of Norwegian HAT patients will be based on a prospective collection of both qualitative and quantitative variables from treatment inclusion and throughout the project period. For the cohorts of Danish patients, of Norwegian patients that have been referred to but not granted HAT, and of Norwegian patients in conventional OAT, data are mainly based on national registries.

In-depth and semi-structured interviews and observation

The qualitative part of the project includes individual in-depth and semi-structured interviews with patients and relatives on their views and experiences with HAT, and focus group interviews with staff concerning implementation, clinical and legal aspects of the project. Semi-structured interview protocols have been developed by the project group and user representatives. Interviews will include 25–35 patients and 10–20 family members, conducted by a team of researchers and user representatives at 1, 6, 18 months after patients enter treatment, and with relatives after 4 and 12 months. Focus group interviews with staff will be conducted at 3, 9 and 18 months. Further, the clinic managers are being interviewed at several timepoints from the planning of the clinics and throughout the duration of the project.

For insights into clinic aspects not identified through interviews, researchers will conduct participant observation in the clinics over several periods of 1–2 weeks throughout the study.

Questionnaires

The quantitative part of the project will use similar questionnaires to preceding projects involving patients in conventional OAT. These will evaluate changes in physical and mental health, personal economy, utilization of social services, criminal behavior and illegal drug use by repeated measures administered at inclusion, by 3, 6 and 12 months of treatment, and thereafter yearly (24, 36 and 48 months). Staff are asked to complete a separate questionnaire if a patient leaves treatment.

Clinical records

Information will also be obtained from the individual patient’s routine clinical records on variables such as main vital signs, nutritional status, cognitive function and mental health, medication, and comorbidities, as well as more HAT-specific variables such as adverse events, dosage, and administration routes of the pharmaceutical heroin.

Central register databases

Nordic national registers are an important and useful source for epidemiological and healthcare services research, including the study of substance use disorders [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. The project will utilize databases from national registries in both Norway and Denmark to describe the cohorts and to monitor the changes and outcomes in a wider context. Currently, one study has explored the use of the Short Form (SF-36) Health Survey in patients enrolled in the Danish HAT database, finding support for the structural and external validity for its use in HAT [ 44 ].

Table  2 gives an overview of the relevant Norwegian and Danish register databases along with their relevant variables.

Additional studies

Currently, the only planned sub-study is on the pharmacokinetics of heroin and its metabolites, and its subjective effects on patients. Despite its widespread use, the pharmacology of heroin remains poorly understood [ 45 , 46 , 47 ]. A subsample of patients will therefore be invited to participate in this observational study with post-administration blood samples collected at different time points, with analysis of the concentration of heroin and its metabolites together with scales of subjective experience. The study has been granted separate approvals from Norwegian Regional Committee for Medical and Health Research Ethics.

Analysis strategy

Exploration and analysis of data will be both by qualitative and quantitative strategies, for individual patients and at the organizational level.

Qualitative

Treatment satisfaction of patients is particularly significant to the project and is often dependent on the context of factors such as staff, management, and clinical environment [ 48 , 49 ]. Qualitative analyses are widely considered valuable for description of phenomena and hypothesis generation, taking into consideration the natural context in which people and organizations function [ 50 ]. Transcribed interviews will be coded following the principles of a flexible inductive thematic analysis and multidimensional approach [ 51 ].

Quantitative

Given the large amount and comprehensive nature of the data, variables of interest will vary in levels of measurement and distribution, so parametric and non-parametric tests will be used accordingly.

Presentation of cohorts will include descriptive statistics by basic parameters such as mean or medians, standard deviations and ratios, and bivariate analyses by ANOVA and Chi-Square tests. Various advanced methods such as survival analysis and logistic and linear regression modeling will be applied based on the type and distribution of dependent variables and co-variates. To avoid ecological fallacy and nested dimensions, multi-level methods will be applied for analyses of patients in relation to services’ organization [ 52 ]. Given the longitudinal design and to address the repeated measurements and correlated data, linear mixed models (LMM) (random intercepts or random slope models) will be used for person-specific effects, and marginal models like Generalized Estimating Equations (GEE) for population effects.

A theoretical sample size for statistical power will not be calculated because the study is based on the total clinical population available. For analyses of discrete and possible repeated events such as the number of criminal acts or medical prescriptions, statistical power will most likely be sufficient even with a restricted number of individuals. For analyses where the proportion of patients to number of variables may imply low statistical power, stratification of the study-population and restrictions to the number of covariates in the multivariate models will be applied.

Economic evaluations

Health economics and methods of cost-effectiveness analysis can guide decision makers, but at the same time they intrinsically rely on sets of politically and administratively determined rules and contexts [ 53 ]. In general, the cost-effectiveness of a treatment is intended to reflect the difference between the recourse’s opportunity costs (medical heroin) and those of the foregone or conventional alternative, to capture a broader set of values beyond the scope of mere financial costs [ 54 ].

Initially, for operating costs a three-step, top-down methodology used and refined by a former healthcare services project will be applied, where total costs are distributed on service units and units of treatment for individual patients [ 55 ].

For cost-effectiveness analyses of healthcare interventions, outcome is often measured in quality-adjusted life years (QALYs) for individual patients, in number of accidents or fatal incidents, or as societal costs associated with patients’ level of functioning and societal (criminal) behavior [ 56 ]. This will readily apply to the project and is in line with the national Norwegian recommendations for evaluation of new health interventions [ 57 , 58 ]. The relationships between HAT and various forms of criminal behavior (both property crime and illegal drug offences), labor market attachment, income and drug expenditures are also unclear and possible subjects for investigation during the project [ 20 ].

The data for all analyses will come from key account figures and relevant variables already obtained in the project.

The main strength of the study comes from its clinical and longitudinal approaches. The use of patient-interviews combined with clinical records, self-report data and register-based information will enhance the analyses and may uncover important associations between the individual patients, treatment, and the organizational level of healthcare services. The results are therefore expected to address aspects of HAT that may contribute to the development of clinical services and individually tailored treatments for OUD.

Study limitations are mainly related to the designs’ limitation for isolation of the effects from HAT on the outcome variables. Although valuable associations often have been suggested by longitudinal ecological studies, this limited possibility of unbiased causal inference remains a major weakness of both epidemiological and cohort designs [ 59 ]. Consequently, analyses will be cautiously interpreted within the context of previous findings, as well as patient and staff experiences. The triangulation of different types of data sources and cohorts, with the use of multivariate analysis and modeling might nevertheless provide more nuanced insights than currently exist.

Also, socially desirable bias concerning self-report questionnaires may be inherent in all self-reported outcomes [ 60 ]. This will apply to the study, as patients in the Norwegian cohort are possibly aware that the prospects of HAT may depend on the results from the study.

The sample of patients in the main cohort might also not be representative of individuals with OUD who do not seek the HAT option for reasons related to the study outcomes, such as social deprivation and isolation, behavioral misconduct, and incarceration [ 61 , 62 ]. Comparison with patients not granted access to the HAT-treatment may partly address this, although not to a full extent.

Lastly, the results will emerge in the context of a Nordic cultural and political system with healthcare reimbursements, insurance models and legal aspects that may limit their generalizability to other countries and societies. Given a cautious interpretation, the project may nonetheless be considered relevant to populations where OAT is used, and a wide range of medications are potentially provided.

Results from this project have the potential to identify new insights of value to patients, healthcare personnel, service administrators and policy makers as to whether an option for pharmaceutical heroin could be implemented as a conventional part of OAT services. We believe that the results will suggest future themes for research within the field of HAT with a potential for individually tailored treatment and care for individuals with OUD. This could affect considerations about drug treatment even beyond HAT-specific topics, where an expanded understanding of why some patients do not succeed with conventional OAT or specific OAT medications will strengthen the knowledge base for drug treatment in general.

Data availability

Data sharing is not applicable to this article as no datasets are currently completed or analyzed. The data that support the eventual findings of this study are available from both national registries, individual health journals and the project-specific database, but restrictions apply to the availability which are under license for the current study. Data may be available from the authors upon reasonable request and dependent on permissions from the Norwegian Regional Committee for Medical and Health Research Ethics. All information on subjects will be stored in the University of Oslo's secure services for sensitive data (TSD). Files for analysis will not contain directly identifying information of patients. Data will be stored in a non-identifiable way for 15 years after the end of the project.

Abbreviations

Opioid agonist treatment

Opioid use disorder

• Heroin assisted treatment

Generalized Estimating Equations

Linear mixed models

Quality-Adjusted Life Years

Norwegian Regional Committee for Medical and Health Research Ethics

Norwegian Centre for Addiction Research, Oslo

Section for Clinical Addiction Research, Oslo

Bergen Addiction Research Group, Bergen

Centre for Alcohol and Drug Research, Aarhus

Norwegian User-union

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Acknowledgements

We would like to thank the representatives from proLARNett for inputs on the design and aims of the study. Also thanks to associate professor Eva Lassemo at SINTEF-Helse, Norway for inputs on economical analysis.

The project is funded by the Norwegian Directorate of Health for the duration of 4.5 years with an annual limit of 5 million NOK (assignment No.20/00546). No remuneration is planned for the subjects’ participation in the project.

Open access funding provided by University of Oslo (incl Oslo University Hospital)

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LHM wrote and drafted the manuscript with critical input from all the authors. The study was planned and designed by TC, DE, LTF and LECW. The statistical section had essential inputs from FM and LHM, the section on economic evaluation had substantial inputs from OD, FM and LHM. The litterature search was conducted LHM, with inputs from TC and LECW. Authors OD, SDP, RE, MH, BT, TLK, EAA and AO read the manuscript and had substantial contributions on data-aquisition and corresponding background material.

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Myklebust, L.H., Eide, D., Arnevik, E.A. et al. Evaluation of heroin-assisted treatment in Norway: protocol for a mixed methods study. BMC Health Serv Res 24 , 398 (2024). https://doi.org/10.1186/s12913-024-10767-w

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Published on 29.3.2024 in Vol 26 (2024)

Telehealth Care Through Internet Hospitals in China: Qualitative Interview Study of Physicians’ Views on Access, Expectations, and Communication

Authors of this article:

Original Paper

• Yuqiong Zhong 1, 2 , Mphil   ; 
• Jessica Hahne 3 , MA, MPH   ; 
• Xiaomin Wang 4, 5 , PhD   ; 
• Xuxi Wang 1 , Bphil   ; 
• Ying Wu 1 , MPhil   ; 
• Xin Zhang 2, 6 * , MD, PhD   ; 
• Xing Liu 6, 7 * , PhD  

1 School of Humanities, Central South University, Changsha, China

2 Xiangya Hospital, Central South University, Changsha, China

3 Department of Psychological & Brain Sciences, Washington University in St Louis, St Louis, MO, United States

4 Center for Clinical Pharmacology, The Third Xiangya Hospital of Central South University, Changsha, China

5 Center for Medical Ethics, Central South University, Changsha, China

6 Medical Humanities Research Center, Central South University, Changsha, China

7 Office of International Cooperation and Exchanges, Xiangya Hospital, Central South University, Changsha, China

*these authors contributed equally

Corresponding Author:

Xing Liu, PhD

Office of International Cooperation and Exchanges

Xiangya Hospital, Central South University

No 87 Xiangya Road, Kaifu District

Changsha, 410008

Phone: 86 18229765509

Email: [email protected]

Background: Internet hospitals in China are an emerging medical service model similar to other telehealth models used worldwide. Internet hospitals are currently in a stage of rapid development, giving rise to a series of new opportunities and challenges for patient care. Little research has examined the views of chronic disease physicians regarding internet hospitals in China.

Objective: We aimed to explore the experience and views of chronic disease physicians at 3 tertiary hospitals in Changsha, China, regarding opportunities and challenges in internet hospital care.

Methods: We conducted semistructured qualitative interviews with physicians (n=26) who had experience working in internet hospitals affiliated with chronic disease departments in 3 tertiary hospitals in Changsha, Hunan province, south central China. Interviews were transcribed verbatim and analyzed by content analysis using NVivo software (version 11; Lumivero).

Results: Physicians emphasized that internet hospitals expand opportunities to conduct follow-up care and health education for patients with chronic illnesses. However, physicians described disparities in access for particular groups of patients, such as patients who are older, patients with lower education levels, patients with limited internet or technology access, and rural patients. Physicians also perceived a gap between patients’ expectations and the reality of limitations regarding both physicians’ availability and the scope of services offered by internet hospitals, which raised challenges for doctor-patient boundaries and trust. Physicians noted challenges in doctor-patient communication related to comprehension and informed consent in internet hospital care.

Conclusions: This study explored the experience and views of physicians in 3 tertiary hospitals in Changsha, China, regarding access to care, patients’ expectations versus the reality of services, and doctor-patient communication in internet hospital care. Findings from this study highlight the need for physician training in telehealth communication skills, legislation regulating informed consent in telehealth care, public education clarifying the scope of internet hospital services, and design of internet hospitals that is informed by the needs of patient groups with barriers to access, such as older adults.

Introduction

As information technology develops rapidly in the current era, telehealth is growing exponentially in use [ 1 - 3 ]. Particularly in the wake of the COVID-19 pandemic, the use of telehealth for both primary and specialist care has accelerated around the globe [ 4 ]. In particular, telehealth is being implemented at an increasing scale in various countries with aging populations to improve health care access and quality for growing numbers of patients with chronic diseases [ 5 - 9 ].

The internet hospital is 1 major emerging telehealth model that is distinct to China, a country with a particularly large aging population and a high chronic disease burden [ 10 - 12 ]. Designed to make health care services more available, convenient, affordable, and efficient, internet hospitals are a type of online platform through which certain health care services can be conducted remotely. There are 3 main types of internet hospitals—those initiated by physical hospitals, those jointly established by physical hospitals and business enterprises, and those initiated by business enterprises relying on physical medical institutions. Research suggests that internet hospitals initiated solely by physical hospitals are the most widespread type [ 13 ]. In terms of the target patient population, internet hospitals primarily aim to facilitate services for patients with common illnesses requiring relatively simple treatment [ 14 ], patients with chronic diseases (diabetes, hypertension, and cancer) [ 15 ], and patients in remote and rural areas [ 16 ].

However, the scope of internet hospitals goes beyond telemedicine services for patients. Services provided by internet hospitals can be classified into three categories, (1) “core medical services,” which mainly include follow-up care for in-person medical services, telemedicine consultations, guidance on chronic disease management, and guidance on medication use; (2) “non-core medical services,” which mainly include medical consultations between health care providers and remote education for health care providers; and (3) “convenience services,” which mainly include health care appointment scheduling, mobile payment for health services, remote examination of medical test results, and dispensation and distribution of some medications [ 10 , 13 , 17 , 18 ]. Thus, the internet hospital model has the potential to increase access to health care for patients and training for providers, and to decrease costs across the health care system.

A number of recent policies by the Chinese national government have promoted rapid development and uptake of the internet hospital model. In 2015 [ 19 ] and 2018 [ 20 ], the State Council issued guidelines promoting “‘Internet +’ Healthcare,” which emphasized the development of internet hospitals as part of the “Health China” strategy for health care reform. Concurrently in 2018, the National Health Commission formulated specific regulations on internet hospital management, which officially authorized internet hospitals to facilitate a range of telehealth services and marked the start of their standardized development [ 21 - 23 ]. In 2020, the National Health Commission issued the “Notice on Strengthening Informatization to Support the Prevention and Control of the Novel Coronavirus Pneumonia Epidemic,” emphasizing the advantages of internet hospitals in controlling the spread of the COVID-19 pandemic [ 24 ]. In the wake of these policies, by June 2023, the number of internet hospitals had reached more than 3000, and 364 million of China’s 1.079 billion internet users were using online medical services [ 25 , 26 ]. However, research suggests that most internet hospitals are not yet fully developed or providing the full scope of services intended to achieve these goals [ 27 - 29 ].

At this early stage of the model’s development, little research to date has evaluated the views of Chinese medical professionals and patients regarding internet hospitals. However, research on telehealth in other countries reveals that telehealth raises many new concerns and challenges alongside the aforementioned opportunities [ 30 - 32 ]. One of the most common concerns raised by patients is the potential for misdiagnosis due to the inability to conduct physical examinations through telehealth [ 33 , 34 ]. Particular groups such as lower-income older adults also commonly report barriers to the use of telehealth such as lack of familiarity with technology or limited access to technological devices or internet connections [ 35 ]. Smartphone data or internet connection problems can also lower patient satisfaction and limit access among rural patient populations [ 36 , 37 ]. Various groups of patients also commonly report feeling concerned about patient privacy and the security and protection of medical data when using telehealth [ 38 ].

In order to guide the direction of internet hospital development in China, further research is needed to examine the emerging challenges and opportunities to patient care presented by this country-specific telehealth platform. The aim of this study was to explore the experience and views of chronic disease physicians at 3 tertiary hospitals in Changsha, China, regarding opportunities and challenges presented by internet hospital care.

The methodology whereby this study was designed and conducted is reported following the items in the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist [ 39 ]. See Multimedia Appendix 1 [ 39 ] for more information.

Setting, Participant Recruitment, and Eligibility Criteria

We conducted in-depth, semistructured interviews with physicians at 3 tertiary hospitals in Changsha, Hunan Province, south central China. Inclusion criteria for participants were 18 years of age or older, experience working in internet hospitals, and employment in a chronic disease department at one of the study hospitals. Our rationale for these inclusion criteria was to select doctors who had work experience relevant to the research questions. Enrollment occurred over a 2-month period from April to May 2022. Using a purposive sampling approach, we obtained a list of doctors who had previous experience working in internet hospitals. We then messaged or called the doctors on the list to briefly explain the primary and secondary objectives of the study, invited them to share their perspectives related to the study, and asked them to be available for interviews. Out of the 28 doctors contacted, 26 agreed to participate, while 2 physicians declined due to a lack of time. Those who responded positively to the invitation were subsequently contacted by the author, YZ, either via WeChat (Tencent Holdings Limited) or telephone to schedule an interview.

We recruited participants until reaching data saturation, at which point no new information about the meaning of codes or themes and the relationship between them continued to appear [ 40 ].

In order to allow spontaneous answers and mitigate bias, participants were given minimum information in advance about specific interview topics.

Ethical Considerations

In April 2022, the research protocol was approved by the institutional review board of Xiangya Hospital, Central South University (#202204092). No prior relationships existed between study participants and members of the research team. Verbal informed consent was recorded via an audio recorder for each participant before participation. Participants were informed in advance that their interviews would be recorded, with the assurance that these recordings would be subject to encryption for security purposes, and they provided their verbal consent accordingly. All participants received a compensation of 200 RMB (1 CNY=US $0.15 on May 2022) for their participation, which was disbursed through a WeChat transfer. To protect the information of the interviewees, the interview data were deidentified in the process of transcription from audio recordings.

Data Collection

The interview guide was collaboratively developed and then subjected to pilot-testing by the research team. Throughout the concurrent phases of data collection and analysis, the interview guide underwent iterative refinement in response to emerging insights and participant responses. This adaptive approach is considered vital to the robustness of qualitative research [ 41 ]. Revisions were implemented subsequent to discussions involving YZ, JH, and Xiaomin Wang, aiming to clarify, define, and critically examine emerging content from interviews as relevant to the research questions. All questions from the finalized interview guide are listed in Textbox 1 .

Q1: What are your views on internet hospitals?

Q2: Could you tell me about your experience working in internet hospitals?

Q3: What do you think are the biggest advantages of internet hospitals? Can you give some examples?

Q4: What do you think are the most troubling or difficult aspects of internet hospitals? Can you give some examples?

Q5: How do you inform the patients who come to the internet hospitals before treatment?

Q6: Do you think there are any differences between doctor-patient communication in internet hospitals and physical hospitals?

Q7: What impact do you think internet hospitals have on doctor-patient communication? Can you give some examples?

Q8: What training have you participated in regarding internet hospitals? What do you think of this training?

Q9: What do you think about the current status of the development of internet hospital laws and regulations in China? How could they be improved? What other aspects can promote the further development of internet hospitals?

Q10: Is there anything else you would like to add about doctor-patient communication in internet hospitals?

Data for this study were collected from April to May 2022. We carried out interviews through a combination of online and offline modalities, depending on each participant’s preference and availability. Online interviews were conducted remotely by video call, via the mobile app WeChat. Offline interviews were conducted in private rooms at the study hospitals. The interviews were conducted in Mandarin Chinese by the authors, YZ (a postgraduate student) and Xiaomin Wang (an associate professor). Both interviewers have received professional training in qualitative interviewing and had extensive experience conducting qualitative research prior to this study.

The research team discussed possible probes and follow-up questions before beginning interviews, and interviewers used them when necessary to draw out more information relevant to the main research question. Concurrently, a second interviewer assumed the role of an observer to ensure the standardization of interview methods and to mitigate potential biases.

Interviews were audio recorded, transcribed, and uploaded into qualitative data management NVivo software (version 11; Lumivero) on password-protected computers to facilitate the analysis. Field notes made by interviewers during the interview process were also stored on password-protected computers, to be used for reference by the research team during analysis. Interviews ranged from 20 to 50 minutes long. Transcripts were sent to participants upon request, but no corrections, comments, or notes were made to transcripts.

Data Analysis

Analysis of the data was performed through conventional content analysis, using guidelines described by Hsieh and Shannon [ 42 ]. An advantage of conventional content analysis is that it avoids using preconceived categories, to generate codes inductively from the data. This modality is considered appropriate when current knowledge of the phenomenon being researched is limited [ 42 ].

Authors YZ and Xuxi Wang transcribed all interviews verbatim and reviewed transcripts several times to acquire a thorough understanding of the whole data set. They then read transcripts line-by-line and highlighted keywords and sentences from a set of initial transcripts, to generate primary codes that captured key concepts. Primary codes were repeatedly reviewed and revised through discussion among the authors and comparison across the transcripts. A finalized codebook including 17 codes and 61 subcodes was used to code all interviews, using NVivo software (version 11). Data saturation was reached after 26 interviews, once the research team determined by the consensus that we had interviewed a sufficiently varied sample of physicians from the 3 study hospitals, while also having obtained sufficiently content-rich data.

Following the coding of all transcripts, all coded segments of the interview data were translated into English by authors YZ and Xuxi Wang, native Mandarin speakers, and double-checked for accuracy by author JH, a native English speaker. Codes and subcodes were repeatedly reviewed and were grouped into clusters according to similarities and differences. Clusters of codes were then treated as subcategories and aggregated into the main categories that were representative of the key findings. These categories were repeatedly reviewed until fully developed, through a process of identifying and comparing exemplary excerpts for each code, category, and subcategory.

This process of analysis culminated in three main categories describing the experiences and views of chronic disease physicians regarding the opportunities and challenges presented by internet hospital care, (1) advancements and shortcomings in care access due to internet hospitals, (2) patients’ expectations versus limitations on doctors’ availability and the scope of services—implications for doctor-patient boundaries and trust, and (3) advantages and downsides of online communication for comprehension and informed consent. These main categories are shown in Textbox 2 , alongside the subcategories from which they were aggregated, and further explained in the results narrative below.

Advancements and shortcomings in care access due to internet hospitals

• Enhanced ability to conduct follow-up care for patients with chronic illness
• More efficient channels for health education
• Disparities in access (ie, for older adults, patients with lower education levels, patients with limited internet or technology access, rural patients)

Patients’ expectations versus limitations on doctors’ availability and the scope of services

• Patients’ expectations of doctors’ availability create unclear professional boundaries.
• Patients’ expectations of the service scope of internet hospitals affect doctor-patient trust.

Advantages and downsides of online communication for comprehension and informed consent:

• Doctors value having extra time to think carefully about replies to patients’ messages, compared to in-person communication.
• Internet hospitals’ restrictions on consultation times, procedures, and arbitrary rules or schedules can hinder effective patient communication.
• Doctors have concerns about the quality of online diagnoses and advice, as well as patient accuracy and comprehension, due to the limitations of online care.
• Doctors have concerns about the completeness and uniformity of clinical informed consent in internet hospitals.

Description of Study Participants

The 26 participants came from 3 different affiliated hospitals with 10, 5, and 11 participants interviewed from each hospital, respectively. Participants ranged in age from 29 to 49 years, and all 26 participants had PhD degrees. Only 5 participants stated that they had received specific training for working in internet hospitals, and 1 of them stated that training included discussion of clinical ethics in internet hospital care. We interviewed doctors from several departments involved in care for patients with chronic disease—oncology, cardiovasology, hematology, endocrinology, gastroenterology, nephrology, and infection departments. Aggregated participant characteristics are presented in Table 1 .

a Some percentages may not add up to 100 due to rounding.

Advancements and Shortcomings in Care Access Due to Internet Hospitals: Follow-Up Care, Health Education, and Disparities

Most doctors stated that internet hospitals affiliated with their physical hospitals of employment were still in the early stages of development and that their internet hospital work experience mainly took place in enterprise-initiated internet hospitals. Doctors stated that internet hospitals initiated by physical hospitals were “not fully operational yet,” (Dr B) and “the consultation volume of patients is relatively small” (Dr C). They also suggested that there was currently a “lack of incentives” (Dr D) to work in internet hospitals initiated by physical hospitals, whereas enterprise-initiated internet hospitals offered “higher income” (Dr B) and a setting where “doctors set their own prices” (Dr C).

In both internet hospitals initiated by physical hospitals and enterprise-initiated internet hospitals, doctors stated that the majority of their work consisted of online consultation for common or easily diagnosable diseases, and follow-up services for patients with chronic diseases who had previously received care at physical hospitals, such as adjusting medications and ordering medical tests to be scheduled in person. Most doctors were motivated to work for internet hospitals particularly because of the opportunity to be part of expanding follow-up care for patients with chronic diseases.

Most of the patients who come to the internet hospitals are chronic disease patients, with conditions such as hypertension, diabetes, coronary heart disease, etc. These patients have been clearly diagnosed in our hospital, and some of them need to be guided or communicated with about what needs to be paid attention to in the process of home-based management. For example, patients' blood pressure might fluctuate, or they can consult online if they have any uncomfortable symptoms, which is quite common. [Dr X]

Some doctors also believed based on experience that internet hospitals could serve as a more efficient channel to provide health education for patients, particularly for the management of chronic diseases.

We also feel that doctors in tertiary hospitals do not have much time to do health education with patients, but through the internet hospitals platforms, we can explain to patients the concept of health or a healthy way of life. For example, for a patient with heart failure, he isn’t expected to come back to the hospital again and again, because I have instilled him with an understanding of healthy lifestyle and diet, and the workload of the doctor will be reduced in the long run. [Dr C]

Despite the ways in which doctors felt internet hospitals expanded access to care and services, they had also observed disparities in access to internet hospitals across several groups, including older adults and patients with lower levels of education or technological literacy: “Older patients may not use smartphones or might need assistance to do so from family members” (Dr E).

Relatively speaking, if the patients come to the internet hospital for consultation, the education level of these patients will be higher, otherwise they will not be able to fully communicate with their doctor. [Dr O]

Most doctors mentioned that internet hospitals are especially suitable for patients with chronic diseases. Doctors also stated that while older adults are one of the most common groups of patients with chronic diseases, many older adults have difficulties in using or accessing internet hospitals (Dr D and Dr X). Some doctors mentioned that internet hospitals currently have limited connections and overlap with local health services in rural areas. They believed moving toward more connection with local services was an important goal—particularly because the demand at tertiary hospitals frequently outstrips resources (Dr P and Dr Q), and because many rural patients travel long distances to receive care at tertiary hospitals.

Even for follow-up visits for chronic diseases or common diseases, many patients will still go to tertiary hospitals. Instead, the patient can go to a qualified local hospital and send us the results of the test, and then [through internet hospital care,] we can advise the patient or tell him how to adjust the medication, or refer the patient to a tertiary hospital for testing. But at present, internet hospitals have not played a big enough role in these aspects. [Dr E]

Patients’ Expectations Versus Limitations on Doctors’ Availability and the Scope of Services: Implications for Doctor-Patient Boundaries and Trust

Patients’ expectations of doctors’ availability create unclear professional boundaries.

When asked about new challenges in patient care posed by internet hospitals, only 1 doctor mentioned risks related to patient privacy and data security.

The internet hospital platform where I am located requires patients to provide their name, gender, age, ID number, and other information, which can be seen on the doctor's portal, but as a doctor I will definitely not disclose the patient's private information but just give him diagnostic advice according to the necessary information provided by the patient. [Dr G]

By contrast, many doctors expressed concerns about their own privacy. Some doctors shared stories from their work in physical hospitals of willingly sharing their personal WeChat with patients in case patients had questions after discharge (Dr D, Dr M, and Dr R). While some doctors did not seem to mind-bending this boundary with patients, others remembered negative experiences when patients had sent messages making demands of doctors’ time at all hours (Dr E, Dr M, Dr P, and Dr Z). They also recalled times when patients obtained the doctor’s personal contact information through their own means and contacted them after leaving the hospital without the doctor’s consent (Dr V and Dr Z). As a result, some doctors had positive views of internet hospitals because they can serve as a means for online communication with patients without requiring the doctor to disclose their own personal contact information.

I prefer to use the official platform to communicate with patients, rather than through private WeChat or phone, because I really don't want to receive phone calls or text messages from patients after I work. But if the phone does ring, I will take into account that he is an old patient of mine, and I will still answer it, because I am not sure if there is any emergency. But for patients on such online hospital platforms, I rarely give them my phone number and personal WeChat. [Dr P]

Several doctors were also uncomfortable that they were required to post information about themselves when working on internet hospital platforms, such as their name, location, and credentials (Dr P and Dr X). Because the audience of patients in internet hospitals is wider, they worried that patients who were dissatisfied with care may have the ability to post negative information about them in public forums online, citing their personal information. As a result, doctors stated that they would be more cautious in diagnosis and giving advice when dealing with patients in internet hospitals with whom they were less familiar (Dr J, Dr O, and Dr V).

Patients’ Expectations of the Service Scope of Internet Hospitals Affect Doctor-Patient Trust

Another concern expressed by many doctors was that patients held unrealistic expectations of the scope of services that internet hospital doctors provide. Some doctors mentioned that some patients feel that just because they spend money in an internet hospital, they should be able to get all their problems solved at once or get immediate treatment (Dr E and Dr G)—when in reality in many cases, the doctor might need to conduct tests over multiple online consultations or might recommend that patients seek further medical services in offline, physical settings. Doctors were concerned that patients’ dissatisfaction with unmet expectations might generate distrust toward the doctor.

What patients don't know is that in fact, most of the time, seeing a doctor is a step-by-step process, and it is necessary to do examinations step-by-step to exclude diseases or diagnose diseases. They often have high expectations for the effect of consultation in internet hospitals, and they think that doctors should be able to diagnose their diseases at one time; and not only to diagnose them, but also to propose a treatment plan. [Dr E]

Some doctors suggested that this gap between expectations and reality could be especially strong for new patients whom the doctor had not seen previously for in-person care. They described that they often recommend for new internet hospital patients to go to physical hospitals to be examined before receiving further internet hospital care or advice, and that patients who are expecting immediate solutions can find this disappointing (Dr U and Dr X). One doctor suggested the need to educate patients and the general public on the scope of internet hospital care, in light of this mismatch in patients’ and doctors’ expectations (Dr Y).

However, some doctors raised concerns about issues related to doctor-patient trust that had less to do with adjusting patients’ expectations and more to do with the format of online communication itself.

Doctor-patient trust will be better in physical hospitals. Because the doctor-patient relationship is a very special relationship, offline communication can observe the patient's expression, speed of speech, action, etc, and is more suitable for empathy with patients. When it comes to the doctor-patient relationship and trust, I think face-to-face consultation is still necessary. [Dr J]

Face-to-face communication in physical hospitals may be more detailed, because if it is through text messages or phone calls, I may be able to talk to the patient in a few words, but if the patient is communicating in our hospital, it may take me half an hour. Because there is unequal information in medicine itself, the patient himself is not very clear about medicine, and without adequate communication, there is no trust between doctors and patients. [Dr W]

Advantages and Downsides of Online Communication for Comprehension and Informed Consent

Doctors working in internet hospitals mainly used pictures and texts, and rarely video calls, to communicate with patients. Some doctors valued the extra time gained by this format to think carefully about their replies to patients’ messages (Dr E, Dr U, and Dr X). However, most doctors pointed out how the lack of nonverbal communication could increase miscommunication and misunderstandings.

Online communication is through typing, and some doctors can't see the facial expressions of patients, which is very inconvenient. The communication between doctors and patients may need body language, facial expressions and other aspects.... I want the patient to really understand me in terms of attitude or tone or feeling or whatever. [Dr G]

Doctors also expressed dissatisfaction with limitations on the time and procedures for consultation through various internet hospitals, and how sometimes arbitrary rules or schedules hindered communication with patients.

The doctors in our department need to be on duty every month in the internet hospitals. When it is my turn to be on duty, a patient will send his questions to me through the platform, but I think this mode is not good. For example, the patient might leave a message for me, but I am busy and don’t reply to him in time, and he may not see my reply in time when I reply. If I go back and forth with him several times, this problem will not be solved until I come back on duty next month, and then the patient's problem will not be solved at all. [Dr C]

The internet hospital at our hospital stipulates that patients can ask five questions at a time.... Sometimes doctors are not able to inquire in detail in order to understand the condition. [Dr G]

Doctors had concerns about the quality of diagnoses and advice that they provided online, due to the inability to do direct physical examination. These concerns were intensified by their perception that many patients could not describe their symptoms clearly and accurately.

Currently, a lot of people still lack of basic medical knowledge, which will lead to ineffective or inefficient consultation on the internet, because they cannot describe their own symptoms, or cannot collect their own data and then summarize it. Patients cannot provide information about their condition sufficiently and accurately, which will seriously affect the efficiency of consultation. [Dr E]

Two doctors also specifically mentioned the difficulty in internet hospital care of not being able to use the “four-diagnosis method”—a method used by doctors in traditional Chinese medicine for diagnosing illness, including diagnosis through observation, diagnosis through auscultation and olfaction, diagnosis through inquiry, and diagnosis through pulse feeling [ 43 ]. Although doctors in this study practiced mainly “Western” medicine, they described integrating certain traditional practices such as this method into their care at physical hospitals (Dr D and Dr H).

In light of concerns about the potential for miscommunication with patients, a few doctors also expressed uncertainty about the completeness and uniformity of clinical informed consent as it is currently practiced in internet hospitals. While they believed that a standardized process of online informed consent for medical advice and treatment was needed, they did not know of any relevant laws or procedures.

Because we have so little time to communicate online, and such a narrow scope of care services, we don't usually obtain informed consent online. I might listen to the patient explain his symptoms. I might tell him what tests he needs before I give my advice, or if I'm dealing with a familiar patient, I might just prescribe his medication, so there's no need for informed consent. However, I think how to issue online informed consent, whether online informed consent is legally effective, and how to sign online informed consent all need to be considered. This is also for the protection of medical staff. [Dr U]

Principal Findings

This study sheds light on previously underresearched aspects of internet hospitals in China, as both the first interview study to examine physicians’ perceptions of internet hospitals and one of the few studies on internet hospitals conducted in China outside of its most major cities. Our research revealed that physicians see enhanced opportunities in internet hospitals to conduct follow-up care for patients with chronic illnesses and to provide health education. However, physicians noted disparities in access for different groups, such as older adults, patients with lower education levels, patients with limited internet or technology access, and rural patients. One particularly novel finding was the conflict between patients’ expectations and the reality of limitations on doctors’ availability and the scope of services available through internet hospitals. Physicians perceived that this gap affected both boundaries and trust in the doctor-patient relationship. Physicians also discussed opportunities and challenges in doctor-patient communication, including issues of comprehension and informed consent. Considering that the development of internet hospitals involves multiple industries, including medical institutions, national policymaking departments, and technology providers, we raise several suggestions below on physician training, patient education, regulations, and design, as well as directions for future research.

Training for Doctors

Internet hospital care involves real-time online sharing of medical data. Information about both doctors and patients is centralized and easily accessible to authorized users on the internet hospital platform. Some doctors in our study were uncomfortable when required to publicly post their names, basic personal information, and credentials on internet hospital platforms because it might make them more vulnerable to public criticism. Doctors’ reasons for being concerned about this were in line with previous research showing a high degree of conflict in the doctor-patient relationship in China [ 44 , 45 ]. This underscores the importance of current efforts both locally and internationally that aim to rebuild trust in the doctor-patient relationship [ 46 - 48 ]. Considering doctors’ concerns about patients requesting for them to disclose their WeChat in both physical and internet hospital work, communication skills training for doctors should prepare doctors for how to interact with patients with empathy and care, while also maintaining their preferred professional boundaries.

It was also notable that only 1 doctor who was interviewed discussed concerns related to patient privacy and data security when asked about challenges presented by internet hospitals. By contrast, Li et al’s [ 49 ] study on the determinants of patients’ use of internet hospitals in China showed that while patients generally desire to use internet hospitals, they are apprehensive about the associated risk of their personal information being leaked. Due to the heightened potential for data leaks and breaches of patient health information associated with the use of internet hospitals, it is imperative that health care professionals undergo training to raise their awareness of data security precautions. For instance, physicians should be trained on proactive measures that they can take to guarantee that the internet hospital services they are affiliated with implement adequate security protocols around patient information. Furthermore, physicians should be trained to communicate with internet hospital patients or their legal proxies about potential risks related to data security and to apprise them of protective measures enacted to safeguard information. Future research should also evaluate the frequency with which data leaks and breaches in internet hospitals actually occur.

Findings from our study also suggest that internet hospitals have led to changes in doctor-patient communication. Doctors in our study considered it to be an advantage of internet hospital care that they generally had more time to communicate with patients compared to in-person care. However, a previous study on internet hospitals suggested that while doctors can obtain key information from patients within a few minutes through in-person communication and examination, information received in the same amount of time online tends to be more limited [ 43 ]. Research conducted by Deng et al [ 50 ] also highlights that engaging in online consultation work while simultaneously engaging in a main career providing in-person medical consultation may place excessive demands on doctors’ time and energy. This phenomenon of work overload could potentially impede the widespread adoption of internet hospitals and introduce added risk to medical practice.

Relatedly, doctors in our study mentioned that when working in internet hospitals, they could only communicate with patients in the form of text, pictures, or video-based consultations, and that they had to rely largely on patient self-report. Both of these factors caused doctors to worry about the accuracy of their diagnoses. This aligns with recent research showing that about 70% of surveyed health care providers believe communication difficulties between patients and health care providers result in online consultations being insufficient [ 51 ], and about 70% of providers report feeling apprehensive about the possibility of misdiagnosis when providing care through internet hospitals [ 51 ]. Recent research has also found that patients who use internet hospitals have more negative views on the doctor-patient relationship than nonusers—including both interpersonal factors such as the degree to which patients trust doctors and practical factors such as the degree to which patients agree with their doctors’ medical opinions. Studies from other countries have similarly shown that telehealth can present new challenges or deficiencies in communication [ 52 - 55 ].

To address such challenges, telehealth communication competencies need to become a core component of both future research and physician training for internet hospitals in China—just as similar competencies are emerging as a priority for telehealth enhancement around the world [ 56 ]. Physicians providing care through internet hospitals should undergo standardized training for web-based communication skills, as research from other countries suggests such training can adapt interpersonal skills to the telehealth environment [ 57 ] and enhance empathic expression. More training for physicians on this skill set might reduce their apprehension about communicating through internet hospitals and assist them in communicating in a manner that improves outcomes for patients. Considering that doctors in our study expressed concern about patient comprehension and diagnostic accuracy, further research is also needed to evaluate and establish methods for measuring patient satisfaction, patient comprehension of information communicated by doctors, and diagnostic accuracy in internet hospital care. Future research should also examine the feasibility of integrating traditional Chinese medical practices such as the four-diagnosis method into telehealth care in China.

Education for Patients or the Public

Findings from our study highlight new challenges in the doctor-patient relationship posed by internet hospital care. One especially novel finding in our study was doctors’ perception that patients subconsciously expected them to be online 24 hours a day, while doctors actually had limited hours working in the internet hospital and could not meet this expectation. Particularly when patients still needed to ask questions after the end of the physician’s available time for consultation, doctors described the risk of conflicts with patients. These findings suggest many patients may be unaware when message-based interactions with physicians in internet hospitals are discontinuous or asynchronous. Therefore, public information about internet hospitals should specify the boundaries of physicians’ availability for internet hospital consultations. While the scope of services may expand as internet hospitals continue to develop, information disseminated to the public should make it clear that internet hospital care is currently only intended for either follow-up care for previously diagnosed patients with chronic diseases, or for new patients with common and more easily diagnosable conditions. Finally, public education should equip patients or their proxies for distinct ways in which they might self-advocate for optimal care in the context of internet hospitals compared to in-person care. This may involve the development of interventions such as question-prompt lists that are specific to equipping patients for internet hospital consultations.

Regulations and Laws

Doctors in our study believed that difficulties with nonverbal communication in internet hospitals often led to miscommunication and misunderstanding, and many raised concerns that there were no specific laws regulating online doctor-patient communication. As a result, most doctors in our study expressed that they felt they were walking on eggshells concerning possible conflicts with patients. This builds on findings from the “2022 China E-hospital development research report” [ 51 ], in which one of the most common suggestions made by health care providers for the further development of internet hospitals was to standardize legal protection for doctors practicing in internet hospitals. Gaps in relevant laws and regulations may reduce the willingness of risk-conscious clinicians to provide medical services through internet hospitals.

Existing internet hospital laws and regulations in China are still mainly in the trial stage [ 51 , 58 ], and are being outpaced by evolving challenges in internet hospital care. The doctors in our study believed that internet hospitals may increase the difficulty of diagnosis and treatment, increase medical safety risks related to miscommunication, and increase the risk of medical malpractice liability. Research by Zhi et al describes how the inability of doctors to perform physical examinations or certain laboratory or imaging examinations through internet hospitals may compromise the accuracy of doctors’ judgments [ 59 ]. However, the legal responsibilities of physical medical institutions, internet hospitals, and doctors regarding issues such as these have not been fully clarified. We suggest that further refinement and clarification of these and other aspects of internet hospital law will help doctors feel more protected in their work and increase the motivation of doctors to work in internet hospitals.

Doctors in our study mentioned that China also lacks detailed legal provisions on the implementation of online informed consent. Internationally recognized ethics standards highlight 4 core elements of informed consent—capacity to consent, information disclosure, comprehension, and voluntary authorization [ 60 ]. Informed consent issues involved in telehealth in other countries are similar to those described by doctors working in internet hospitals in this study, namely, the degree of discernment required from providers to ensure that patients are sufficiently informed to provide consent increases dramatically in telehealth [ 61 ]. In the United States, different states have different regulations on remote informed consent, and no federal policy has been formed at present. Some states require patients to fill out and sign written consent forms, while others do not [ 62 ]. In China, the Administrative Measures for internet hospitals stipulate that “internet hospitals must warn patients of risks and obtain informed consent from patients” [ 22 ]. However, current laws in China do not provide clear rules regarding the validity of electronic signatures for informed consent in internet hospital care. Informed consent in internet hospital care also involves unique information security issues due to the use of electronic health records, but there is currently no specific legal guidance for internet hospital platform developers or doctors concerning data security protection of informed consent in internet hospital care.

Tackling Disparities

Our research revealed that while older adults are at higher risk for chronic illness and are the main target population for internet hospitals, they are also reported by doctors to experience a number of barriers to internet hospital use. This finding aligns with research from various countries showing that older adults are less likely than younger patients to express positive attitudes toward using telehealth [ 63 , 64 ]. Health care providers in China and other countries have observed that older adults may be apprehensive about telehealth due to difficulty in operating computers or smartphones [ 65 ], may need the help of care partners to log into telehealth accounts successfully, and may need more time on average to download and set up applications [ 43 , 66 ]. Previous surveys have also shown that medical personnel believe that the low efficiency of online communication between doctors and patients and the low internet use rate of some patient groups (such as older adults) are the main factors hindering the development of internet hospitals [ 51 ].

Previous research in various countries has shown that the ease of use and perceived usefulness of telehealth systems have a positive impact on the acceptance of telehealth in patients who are older [ 67 , 68 ]. However, to date, China has not established an effective quality control system for internet hospitals [ 27 , 69 ], and the aforementioned ways in which internet hospitals currently pose increased risks for patient safety may affect general patients’, let alone older adults’ willingness to use them [ 49 ]. We recommend that the needs of older adults be considered in the design and development of internet hospital platforms and that older adults participate in the system design process [ 70 , 71 ]. Community health workers may be a workforce that could be mobilized to support telehealth training efforts among patients who are older, assist individuals with limited telehealth literacy in attending online appointments, and provide culturally and linguistically appropriate information about telehealth to rural patients and communities [ 64 , 72 , 73 ]. In general, health care organizations should invest in developing internet hospitals that are functional and easy to use. Drawing from research on telehealth design improvement in other countries, internet hospitals could be designed with features in mind to help physicians communicate more clearly with patients, such as providing notifications to physicians when patients read messages [ 74 ]. In addition, it may be beneficial for platforms to provide training materials to patients when patients register and log into internet hospitals for the first time [ 75 ]. Considering that a major goal of internet hospital development is to expand health care access, it will be crucial to address disparities in internet hospital use through these and other educational and design considerations.

Limitations

This study should be interpreted in the light of certain limitations. As most participants interviewed were attending physicians, findings may not be generalizable to the perspective of other health care workers or patients. The generalizability of our study findings from a single region and time point may also be limited, as there may be variations in internet hospital features and practice in other regions in China, and over time as internet hospitals continue to develop rapidly.

Conclusions

This study explored the experience and views of physicians in 3 tertiary hospitals in Changsha, China regarding access to care, patients’ expectations versus the reality of services, and doctor-patient communication in internet hospital care. Findings from this study indicate that there is a need to train physicians in telehealth-specific communication skills. National policymaking departments should also further refine laws and regulations concerning internet hospitals, particularly those related to online informed consent. Technology developers should take the needs of older adults into particular account in the design of internet hospital platforms.

Acknowledgments

This study was supported by Major Program of National Social Science Fund of China, Research on Moral Issues in the Field of Contemporary Science and Technology (22&ZD044), the Xiangya Medical Humanities Series: Principles of Biomedical Ethics (monograph Award) Fund (KTZZPT019), Hunan Provincial Innovation Foundation for Postgraduate (CX20220133), the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0041), the China Scholarship Council (CSC,202206370069), and the Natural Science of Changsha City (kq2202362).

Data Availability

The data generated and analyzed during this study are available from the corresponding author upon reasonable request.

Authors' Contributions

YZ and XL conceptualized this study and designed the methodology. YZ and Xiaomin Wang conducted the interviews for data collection. Xuxi Wang and YZ transcribed the interviews. YZ, Xuxi Wang, YW, and XZ conducted and provided resources for preliminary analysis of the data. YZ and JH wrote and edited the paper. JH, XL, and XZ oversaw the implementation of all study activities. All authors read and approved the final paper.

Conflicts of Interest

None declared.

COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 22.03.23; peer-reviewed by Y Cao, N Mungoli, A Gangadhara Rao; comments to author 06.09.23; revised version received 27.09.23; accepted 26.02.24; published 29.03.24.

©Yuqiong Zhong, Jessica Hahne, Xiaomin Wang, Xuxi Wang, Ying Wu, Xin Zhang, Xing Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.03.2024.

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