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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualize your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, “hours of study,” is the independent variable (the predictor, or explanatory variable)
• The expected effect, “exam score,” is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (“hours of study”).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the “effect” component of the cause-and-effect relationship.

Let’s add the moderator “IQ.” Here, a student’s IQ level can change the effect that the variable “hours of study” has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our “IQ” moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the “IQ” moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs. mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounded assumptions or predictions of behavior. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem. It is the structure of your paper that summarizes concepts, ideas, and theories derived from prior research studies and which was synthesized in order to form a conceptual basis for your analysis and interpretation of meaning found within your research.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (December 2018): 44-53; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013; Varpio, Lara, Elise Paradis, Sebastian Uijtdehaage, and Meredith Young. "The Distinctions between Theory, Theoretical Framework, and Conceptual Framework." Academic Medicine 95 (July 2020): 989-994.

Importance of Theory and a Theoretical Framework

Theories can be unfamiliar to the beginning researcher because they are rarely applied in high school social studies curriculum and, as a result, can come across as unfamiliar and imprecise when first introduced as part of a writing assignment. However, in their most simplified form, a theory is simply a set of assumptions or predictions about something you think will happen based on existing evidence and that can be tested to see if those outcomes turn out to be true. Of course, it is slightly more deliberate than that, therefore, summarized from Kivunja (2018, p. 46), here are the essential characteristics of a theory.

• It is logical and coherent
• It has clear definitions of terms or variables, and has boundary conditions [i.e., it is not an open-ended statement]
• It has a domain where it applies
• It has clearly described relationships among variables
• It describes, explains, and makes specific predictions
• It comprises of concepts, themes, principles, and constructs
• It must have been based on empirical data [i.e., it is not a guess]
• It must have made claims that are subject to testing, been tested and verified
• It must be clear and concise
• Its assertions or predictions must be different and better than those in existing theories
• Its predictions must be general enough to be applicable to and understood within multiple contexts
• Its assertions or predictions are relevant, and if applied as predicted, will result in the predicted outcome
• The assertions and predictions are not immutable, but subject to revision and improvement as researchers use the theory to make sense of phenomena
• Its concepts and principles explain what is going on and why
• Its concepts and principles are substantive enough to enable us to predict a future

Given these characteristics, a theory can best be understood as the foundation from which you investigate assumptions or predictions derived from previous studies about the research problem, but in a way that leads to new knowledge and understanding as well as, in some cases, discovering how to improve the relevance of the theory itself or to argue that the theory is outdated and a new theory needs to be formulated based on new evidence.

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.
• The theoretical framework adds context around the theory itself based on how scholars had previously tested the theory in relation their overall research design [i.e., purpose of the study, methods of collecting data or information, methods of analysis, the time frame in which information is collected, study setting, and the methodological strategy used to conduct the research].

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument. Writing@CSU. Colorado State University; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (2018): 44-53; Omodan, Bunmi Isaiah. "A Model for Selecting Theoretical Framework through Epistemology of Research Paradigms." African Journal of Inter/Multidisciplinary Studies 4 (2022): 275-285; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

• Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
• Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
• Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
• List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
• Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
• Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

• Means by which new research data can be interpreted and coded for future use,
• Response to new problems that have no previously identified solutions strategy,
• Means for identifying and defining research problems,
• Means for prescribing or evaluating solutions to research problems,
• Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
• Means of giving old data new interpretations and new meaning,
• Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
• Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
• Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

• Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
• Position your theoretical framework within a broader context of related frameworks, concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
• The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
• You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
• Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument. Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Other Disciplines

An increasingly important trend in the social and behavioral sciences is to think about and attempt to understand research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be more engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem or, if appropriate, how the theoretical framework was found to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

• A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted among a set of scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis. About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis. Slideshare presentation.

Still Yet Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis will likely include the expectation by your professor that you should offer modifications to the theory based on your research findings.

Indications that theoretical assumptions may need to be modified can include the following:

• Your findings suggest that the theory does not explain or account for current conditions or circumstances or the passage of time,
• The study reveals a finding that is incompatible with what the theory attempts to explain or predict, or
• Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors revealed from your analysis [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

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What Is A Theoretical Framework? A Practical Answer

• Published: 30 November 2015
• Volume 26 , pages 593–597, ( 2015 )

Cite this article

• Norman G. Lederman 1 &
• Judith S. Lederman 1  

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Avoid common mistakes on your manuscript.

Other than the poor or non-existent validity and/or reliability of data collection measures, the lack of a theoretical framework is the most frequently cited reason for our editorial decision not to publish a manuscript in the Journal of Science Teacher Education . A poor or missing theoretical framework is similarly a critical problem for manuscripts submitted to other journals for which Norman or Judith have either served as Editor or been on the Editorial Board. Often the problem is that an author fails to justify his/her research effort with a theoretical framework. However, there is another level to the problem. Many individuals have a rather narrow conception of what constitutes a theoretical framework or that it is somehow distinct from a conceptual framework. The distinction on lack thereof is a story for another day. The following story may remind you of an experience you or one of your classmates have had.

Doctoral students live in fear of hearing these now famous words from their thesis advisor: “This sounds like a promising study, but what is your theoretical framework?” These words instantly send the harried doctoral student to the library (giving away our ages) in search of a theory to support the proposed research and to satisfy his/her advisor. The search is often unsuccessful because of the student’s misconception of what constitutes a “theoretical framework.” The framework may actually be a theory, but not necessarily. This is especially true for theory driven research (typically quantitative) that is attempting to test the validity of existing theory. However, this narrow definition of a theoretical framework is commonly not aligned with qualitative research paradigms that are attempting to develop theory, for example, grounded theory, or research falling into the categories of description and interpretation research (Peshkin, 1993 ). Additionally, a large proportion of doctoral theses do not fit the narrow definition described. The argument here is not that various research paradigms have no overarching philosophies or theories about knowing. Clearly quantitative research paradigms are couched in a realist perspective and qualitative research paradigms are couched in an idealist perspective (Bogdan & Biklen, 1982 ). The discussion here is focused on theoretical frameworks at a much more specific and localized perspective with respect to the justification and conceptualization of a single research investigation. So, what is a theoretical framework?

It is, perhaps, easier to understand the nature and function of a theoretical framework if it is viewed as the answer to two basic questions:

What is the problem or question?

Why is your approach to solving the problem or answering the question feasible?

Indeed, the answers to these questions are the substance and culmination of Chapters I and II of the proposal and completed dissertation, or the initial sections preceding the Methods section of a research article. The answers to these questions can come from only one source, a thorough review of the literature (i.e., a review that includes both the theoretical and empirical literature as well as apparent gaps in the literature). Perhaps, a hypothetical situation can best illustrate the development and role of the theoretical framework in the formalization of a dissertation topic or research investigation. Let us continue with the doctoral student example, keeping in mind that a parallel situation also presents itself to any researcher planning research that he/she intends to publish.

As an interested reader of educational literature, a doctoral student becomes intrigued by the importance of questioning in the secondary classroom. The student immediately begins a manual and computer search of the literature on questioning in the classroom. The student notices that the research findings on the effectiveness of questioning strategies are rather equivocal. In particular, much of the research focuses on the cognitive levels of the questions asked by the teacher and how these questions influence student achievement. It appears that the research findings exhibit no clear pattern. That is, in some studies, frequent questioning at higher cognitive levels has led to more achievement than frequent questioning at the lower cognitive levels. However, an equal number of investigations have shown no differences between the achievement of students who are exposed to questions at distinctly different cognitive levels, but rather the simple frequency of questions.

The doctoral student becomes intrigued by these equivocal findings and begins to speculate about some possible explanations. In a blinding flash of insight, the student remembers hearing somewhere that an eccentric Frenchman named Piaget said something about students being categorized into levels of cognitive development. Could it be that a student’s cognitive level has something to do with how much and what he/she learns? The student heads back to the library and methodically searches through the literature on cognitive development and its relationship to achievement.

At this point, the doctoral student has become quite familiar with two distinct lines of educational research. The research on the effectiveness of questioning has established that there is a problem. That is, does the cognitive level of questioning have any effect on student achievement? In effect, this answers the first question identified previously with respect to identification of a theoretical framework. The research on the cognitive development of students has provided an intriguing perspective. That is, could it be possible that students of different cognitive levels are affected differently by questions at different cognitive levels? If so, an answer to the problem concerning the effectiveness questioning may be at hand. This latter question, in effect, has addressed the second question previously posed about the identification of a theoretical framework. At this point, the student has narrowed his/her interests as a result of reviewing the literature. Note that the doctoral student is now ready to write down a specific research question and that this is only possible after having conducted a thorough review of the literature.

The student writes down the following research hypotheses:

Both high and low cognitive level pupils will benefit from both high and low cognitive levels of questions as opposed to no questions at all.

Pupils categorized at high cognitive levels will benefit more from high cognitive level questions than from low level questions.

Pupils categorized at lower cognitive levels will benefit more from low cognitive level questions than from high level questions.

These research questions still need to be transformed into testable statistical hypotheses, but they are ready to be presented to the dissertation advisor. The advisor looks at the questions and says: “This looks like a promising study, but what is your theoretical framework?” There is no need, however for a sprint to the library. The doctoral student has a theoretical framework. The literature on questioning has established that there is a problem and the literature on cognitive development has provided the rationale for performing the specific investigation that is being proposed. ALL IS WELL!

If some of the initial research completed by Norman concerning what classroom variables contributed to students’ understandings of nature of science (Lederman, 1986a , 1986b ; Lederman & Druger, 1985 ) had to align with the overly restricted definition of a theoretical framework, which necessitates the presence of theory, it never would have been published. In these initial studies, various classroom variables were identified that were related to students’ improved understandings of nature of science. The studies were descriptive and correlational and were not driven by any theory about how students learn nature of science. Indeed, the design of the studies was derived from the fact that there were no existing theories, general or specific, to explain how students might learn nature of science more effectively. Similarly, the seminal study of effective teaching, the Beginning Teacher Evaluation Study (Tikunoff, Berliner, & Rist, 1975 ), was an ethnographic study that was not guided by the findings of previous research on effective teaching. Rather, their inductive study simply compared 40 teachers “known” to be effective and ineffective of mathematics and reading to derive differences in classroom practice. Their study had no theoretical framework if one were to use the restrictive conception that a theory needed to provide a guiding framework for the investigation. There are plenty of other examples that have guided lines of research that could be provided, but there is no need to beat a dead horse by detailing more examples. The simple, but important, point is that research following qualitative research paradigms or traditions (Jacob, 1987 ; Smith, 1987 ) are particularly vulnerable to how ‘theoretical framework’ is defined. Indeed, it could be argued that the necessity of a theory is a remnant from the times in which qualitative research was not as well accepted as it is today. In general, any research design that is inductive in nature and attempts to develop theory would be at a loss. We certainly would not want to eliminate multiple traditions of research from the Journal of Science Teacher Education .

Harry Wolcott’s discussion about validity in qualitative research (Wolcott, 1990 ) is quite explicit about the lack of theory or necessity of theory in driving qualitative ethnography. Interestingly, he even rejects the idea of validity as being a necessary criterion in qualitative research. Additionally, Bogdan and Biklen ( 1982 ) emphasize the importance of qualitative researchers “bracketing” (i.e., masking or trying to forget) their a priori theories so that it does not influence the collection of data or any meanings assigned to data during an investigation. Similar discussions about how qualitative research differs from quantitative research with respect to the necessity of theory guiding the research have been advanced by many others (e.g., Becker, 1970 ; Bogdan & Biklen, 1982 ; Erickson, 1986 ; Krathwohl, 2009 ; Rist, 1977 ; among others). Perhaps, Peshkin ( 1993 , p. 23) put it best when he expressed his concern that “Research that is not theory driven, hypothesis testing, or generalization producing may be dismissed as deficient or worse.” Again, the key point is that qualitative research is as valuable and can contribute as much to our knowledge of teaching and learning as quantitative research.

There is little doubt that qualitative researchers often invoke theory when analyzing the data they have collected or try to place their findings within the context of the existing literature. And, as stated at the beginning of this editorial, different research paradigms have large overarching theories about how one comes to know about the world. However, this is not the same thing has using a theory as a framework for the design of an investigation from the stating of research questions to developing a design to answer the research questions.

It is quite possible that you may be thinking that this editorial about the meaning of a theoretical framework is too theoretical. Trust us in believing that there is a very practical reason for us addressing this issue. At the beginning of the editorial we talked about the lack of a theoretical framework being the second most common reason for manuscripts being rejected for publication in the Journal of Science Teacher Education . Additionally, we mentioned that this is a common reason for manuscripts being rejected by other prominent journals in science education, and education in general. Consequently, it is of critical importance that we, as a community, are clear about the meaning of a theoretical framework and its use. It is especially important that our authors, reviewers, associate editors, and we as Editors of the journal are clear on this matter. Let us not fail to mention that most of us are advising Ph.D. students in the conceptualization of their dissertations. This issue is not new. In 1992, the editorial board of the Journal of Research in Science Teaching was considering the claim, by some, that qualitative research was not being evaluated fairly for publication relative to quantitative research. In their analysis of the relative success of publication for quantitative and qualitative research, Wandersee and Demastes ( 1992 , p. 1005) noted that reviewers often noted, “The manuscript had a weak theoretical basis” when reviewing qualitative research.

Theoretical frameworks are critically important to all of our work, quantitative, qualitative, or mixed methods. All research articles should have a valid theoretical framework to justify the importance and significance of the work. However, we should not live in fear, as the doctoral student, of not having a theoretical framework, when we actually have such, because an Editor, reviewer, or Major Professor is using any unduly restrictive and outdated meaning for what constitutes a theoretical framework.

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Lederman, N.G., Lederman, J.S. What Is A Theoretical Framework? A Practical Answer. J Sci Teacher Educ 26 , 593–597 (2015). https://doi.org/10.1007/s10972-015-9443-2

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What is a Conceptual Framework?

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format.

Updated on August 28, 2023

What are frameworks in research?

Both theoretical and conceptual frameworks have a significant role in research.  Frameworks are essential to bridge the gaps in research. They aid in clearly setting the goals, priorities, relationship between variables. Frameworks in research particularly help in chalking clear process details.

Theoretical frameworks largely work at the time when a theoretical roadmap has been laid about a certain topic and the research being undertaken by the researcher, carefully analyzes it, and works on similar lines to attain successful results. 

It varies from a conceptual framework in terms of the preliminary work required to construct it. Though a conceptual framework is part of the theoretical framework in a larger sense, yet there are variations between them.

The following sections delve deeper into the characteristics of conceptual frameworks. This article will provide insight into constructing a concise, complete, and research-friendly conceptual framework for your project.

Definition of a conceptual framework

True research begins with setting empirical goals. Goals aid in presenting successful answers to the research questions at hand. It delineates a process wherein different aspects of the research are reflected upon, and coherence is established among them. 

A conceptual framework is an underrated methodological approach that should be paid attention to before embarking on a research journey in any field, be it science, finance, history, psychology, etc. 

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format. Your conceptual framework establishes a link between the dependent and independent variables, factors, and other ideologies affecting the structure of your research.

A critical facet a conceptual framework unveils is the relationship the researchers have with their research. It closely highlights the factors that play an instrumental role in decision-making, variable selection, data collection, assessment of results, and formulation of new theories.

Consequently, if you, the researcher, are at the forefront of your research battlefield, your conceptual framework is the most powerful arsenal in your pocket.

What should be included in a conceptual framework?

A conceptual framework includes the key process parameters, defining variables, and cause-and-effect relationships. To add to this, the primary focus while developing a conceptual framework should remain on the quality of questions being raised and addressed through the framework. This will not only ease the process of initiation, but also enable you to draw meaningful conclusions from the same. 

A practical and advantageous approach involves selecting models and analyzing literature that is unconventional and not directly related to the topic. This helps the researcher design an illustrative framework that is multidisciplinary and simultaneously looks at a diverse range of phenomena. It also emboldens the roots of exploratory research. 

Fig. 1: Components of a conceptual framework

How to make a conceptual framework

The successful design of a conceptual framework includes:

• Selecting the appropriate research questions
• Defining the process variables (dependent, independent, and others)
• Determining the cause-and-effect relationships

This analytical tool begins with defining the most suitable set of questions that the research wishes to answer upon its conclusion. Following this, the different variety of variables is categorized. Lastly, the collected data is subjected to rigorous data analysis. Final results are compiled to establish links between the variables. 

The variables drawn inside frames impact the overall quality of the research. If the framework involves arrows, it suggests correlational linkages among the variables. Lines, on the other hand, suggest that no significant correlation exists among them. Henceforth, the utilization of lines and arrows should be done taking into cognizance the meaning they both imply.

Example of a conceptual framework

To provide an idea about a conceptual framework, let’s examine the example of drug development research. 

Say a new drug moiety A has to be launched in the market. For that, the baseline research begins with selecting the appropriate drug molecule. This is important because it:

• Provides the data for molecular docking studies to identify suitable target proteins
• Performs in vitro (a process taking place outside a living organism) and in vivo (a process taking place inside a living organism) analyzes

This assists in the screening of the molecules and a final selection leading to the most suitable target molecule. In this case, the choice of the drug molecule is an independent variable whereas, all the others, targets from molecular docking studies, and results from in vitro and in vivo analyses are dependent variables.

The outcomes revealed by the studies might be coherent or incoherent with the literature. In any case, an accurately designed conceptual framework will efficiently establish the cause-and-effect relationship and explain both perspectives satisfactorily.

If A has been chosen to be launched in the market, the conceptual framework will point towards the factors that have led to its selection. If A does not make it to the market, the key elements which did not work in its favor can be pinpointed by an accurate analysis of the conceptual framework.

Fig. 2: Concise example of a conceptual framework

Important takeaways

While conceptual frameworks are a great way of designing the research protocol, they might consist of some unforeseen loopholes. A review of the literature can sometimes provide a false impression of the collection of work done worldwide while in actuality, there might be research that is being undertaken on the same topic but is still under publication or review. Strong conceptual frameworks, therefore, are designed when all these aspects are taken into consideration and the researchers indulge in discussions with others working on similar grounds of research.

Conceptual frameworks may also sometimes lead to collecting and reviewing data that is not so relevant to the current research topic. The researchers must always be on the lookout for studies that are highly relevant to their topic of work and will be of impact if taken into consideration. 

Another common practice associated with conceptual frameworks is their classification as merely descriptive qualitative tools and not actually a concrete build-up of ideas and critically analyzed literature and data which it is, in reality. Ideal conceptual frameworks always bring out their own set of new ideas after analysis of literature rather than simply depending on facts being already reported by other research groups.

So, the next time you set out to construct your conceptual framework or improvise on your previous one, be wary that concepts for your research are ideas that need to be worked upon. They are not simply a collection of literature from the previous research.

Final thoughts

Research is witnessing a boom in the methodical approaches being applied to it nowadays. In contrast to conventional research, researchers today are always looking for better techniques and methods to improve the quality of their research. 

We strongly believe in the ideals of research that are not merely academic, but all-inclusive. We strongly encourage all our readers and researchers to do work that impacts society. Designing strong conceptual frameworks is an integral part of the process. It gives headway for systematic, empirical, and fruitful research.

Vridhi Sachdeva, MPharm

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What is a good example of a conceptual framework?

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• The importance of a conceptual framework

The main purpose of a conceptual framework is to improve the quality of a research study. A conceptual framework achieves this by identifying important information about the topic and providing a clear roadmap for researchers to study it.

Through the process of developing this information, researchers will be able to improve the quality of their studies in a few key ways.

Clarify research goals and objectives

A conceptual framework helps researchers create a clear research goal. Research projects often become vague and lose their focus, which makes them less useful. However, a well-designed conceptual framework helps researchers maintain focus. It reinforces the project’s scope, ensuring it stays on track and produces meaningful results.

Provide a theoretical basis for the study

Forming a hypothesis requires knowledge of the key variables and their relationship to each other. Researchers need to identify these variables early on to create a conceptual framework. This ensures researchers have developed a strong understanding of the topic before finalizing the study design. It also helps them select the most appropriate research and analysis methods.

Guide the research design

As they develop their conceptual framework, researchers often uncover information that can help them further refine their work.

Here are some examples:

Confounding variables they hadn’t previously considered

Sources of bias they will have to take into account when designing the project

Whether or not the information they were going to study has already been covered—this allows them to pivot to a more meaningful goal that brings new and relevant information to their field

• Steps to develop a conceptual framework

There are four major steps researchers will follow to develop a conceptual framework. Each step will be described in detail in the sections that follow. You’ll also find examples of how each might be applied in a range of fields.

Step 1: Choose the research question

The first step in creating a conceptual framework is choosing a research question . The goal of this step is to create a question that’s specific and focused.

By developing a clear question, researchers can more easily identify the variables they will need to account for and keep their research focused. Without it, the next steps will be more difficult and less effective.

Here are some examples of good research questions in a few common fields:

Natural sciences: How does exposure to ultraviolet radiation affect the growth rate of a particular type of algae?

Health sciences: What is the effectiveness of cognitive-behavioral therapy for treating depression in adolescents?

Business: What factors contribute to the success of small businesses in a particular industry?

Education: How does implementing technology in the classroom impact student learning outcomes?

Step 2: Select the independent and dependent variables

Once the research question has been chosen, it’s time to identify the dependent and independent variables .

The independent variable is the variable researchers think will affect the dependent variable . Without this information, researchers cannot develop a meaningful hypothesis or design a way to test it.

The dependent and independent variables for our example questions above are:

Natural sciences

Independent variable: exposure to ultraviolet radiation

Dependent variable: the growth rate of a particular type of algae

Health sciences

Independent variable: cognitive-behavioral therapy

Dependent variable: depression in adolescents

Independent variables: factors contributing to the business’s success

Dependent variable: sales, return on investment (ROI), or another concrete metric

Independent variable: implementation of technology in the classroom

Dependent variable: student learning outcomes, such as test scores, GPAs, or exam results

Step 3: Visualize the cause-and-effect relationship

This step is where researchers actually develop their hypothesis. They will predict how the independent variable will impact the dependent variable based on their knowledge of the field and their intuition.

With a hypothesis formed, researchers can more accurately determine what data to collect and how to analyze it. They will then visualize their hypothesis by creating a diagram. This visualization will serve as a framework to help guide their research.

The diagrams for our examples might be used as follows:

Natural sciences : how exposure to radiation affects the biological processes in the algae that contribute to its growth rate

Health sciences : how different aspects of cognitive behavioral therapy can affect how patients experience symptoms of depression

Business : how factors such as market demand, managerial expertise, and financial resources influence a business’s success

Education : how different types of technology interact with different aspects of the learning process and alter student learning outcomes

Step 4: Identify other influencing variables

The independent and dependent variables are only part of the equation. Moderating, mediating, and control variables are also important parts of a well-designed study. These variables can impact the relationship between the two main variables and must be accounted for.

A moderating variable is one that can change how the independent variable affects the dependent variable. A mediating variable explains the relationship between the two. Control variables are kept the same to eliminate their impact on the results. Examples of each are given below:

Moderating variable: water temperature (might impact how algae respond to radiation exposure)

Mediating variable: chlorophyll production (might explain how radiation exposure affects algae growth rate)

Control variable: nutrient levels in the water

Moderating variable: the severity of depression symptoms at baseline might impact how effective the therapy is for different adolescents

Mediating variable: social support might explain how cognitive-behavioral therapy leads to improvements in depression

Control variable: other forms of treatment received before or during the study

Moderating variable: the size of the business (might impact how different factors contribute to market share, sales, ROI, and other key success metrics)

Mediating variable: customer satisfaction (might explain how different factors impact business success)

Control variable: industry competition

Moderating variable: student age (might impact how effective technology is for different students)

Mediating variable: teacher training (might explain how technology leads to improvements in learning outcomes)

Control variable: student learning style

• Conceptual versus theoretical frameworks

Although they sound similar, conceptual and theoretical frameworks have different goals and are used in different contexts. Understanding which to use will help researchers craft better studies.

Conceptual frameworks describe a broad overview of the subject and outline key concepts, variables, and the relationships between them. They provide structure to studies that are more exploratory in nature, where the relationships between the variables are still being established. They are particularly helpful in studies that are complex or interdisciplinary because they help researchers better organize the factors involved in the study.

Theoretical frameworks, on the other hand, are used when the research question is more clearly defined and there’s an existing body of work to draw upon. They define the relationships between the variables and help researchers predict outcomes. They are particularly helpful when researchers want to refine the existing body of knowledge rather than establish it.

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A scoping review of frameworks in empirical studies and a review of dissemination frameworks

Ana a. baumann.

1 Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, USA

Cole Hooley

2 School of Social Work, Brigham Young University, Provo, USA

Emily Kryzer

3 BJC HealthCare, Community Health Improvement, St. Louis, USA

Alexandra B. Morshed

4 Rollins School of Public Health, Emory University, Atlanta, USA

Cassidy A. Gutner

5 ViiV Healthcare, Research Triangle Park, NC USA

6 Department of Psychiatry, Boston University School of Medicine, Boston, MA USA

Sara Malone

7 Brown School of Social Work, Washington University in St. Louis, St. Louis, USA

Callie Walsh-Bailey

Meagan pilar.

8 Department of Infectious Diseases, Washington University School of Medicine, Washington University in St. Louis, St. Louis, USA

Brittney Sandler

9 Bernard Becker Medical Library, School of Medicine, Washington University in St. Louis, St. Louis, USA

Rachel G. Tabak

Stephanie mazzucca, associated data.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

The field of dissemination and implementation (D&I) research has grown immensely in recent years. However, the field of dissemination research has not coalesced to the same degree as the field of implementation research. To advance the field of dissemination research, this review aimed to (1) identify the extent to which dissemination frameworks are used in dissemination empirical studies, (2) examine how scholars define dissemination, and (3) identify key constructs from dissemination frameworks.

To achieve aims 1 and 2, we conducted a scoping review of dissemination studies published in D&I science journals. The search strategy included manuscripts published from 1985 to 2020. Articles were included if they were empirical quantitative or mixed methods studies about the dissemination of information to a professional audience. Studies were excluded if they were systematic reviews, commentaries or conceptual papers, scale-up or scale-out studies, qualitative or case studies, or descriptions of programs. To achieve aim 1, we compiled the frameworks identified in the empirical studies. To achieve aim 2, we compiled the definitions from dissemination from frameworks identified in aim 1 and from dissemination frameworks identified in a 2021 review (Tabak RG, Am J Prev Med 43:337-350, 2012). To achieve aim 3, we compile the constructs and their definitions from the frameworks.

Out of 6017 studies, 89 studies were included for full-text extraction. Of these, 45 (51%) used a framework to guide the study. Across the 45 studies, 34 distinct frameworks were identified, out of which 13 (38%) defined dissemination. There is a lack of consensus on the definition of dissemination. Altogether, we identified 48 constructs, divided into 4 categories: process, determinants, strategies, and outcomes. Constructs in the frameworks are not well defined.

Implication for D&I research

This study provides a critical step in the dissemination research literature by offering suggestions on how to define dissemination research and by cataloging and defining dissemination constructs. Strengthening these definitions and distinctions between D&I research could enhance scientific reproducibility and advance the field of dissemination research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13012-022-01225-4.

Contributions to the literature

• The field of dissemination research has not coalesced to the same degree as the field of implementation research. Clearly defining dissemination and identifying dissemination constructs will help enhance dissemination research.
• In a review of 34 frameworks, we found a lack of consensus in the definition of dissemination and 48 constructs identified in the frameworks.
• We provide a suggested definition of dissemination and a catalog of the constructs to advance the field of dissemination research.

The field of dissemination and implementation (D&I) research has grown extensively in the past years. While scholars from the field of implementation research have made substantial advances, the field of dissemination research has not coalesced to the same degree, limiting the ability to conduct rigorous, reproducible dissemination research. Dissemination research has broadly focused on examining how evidence-based information gets packaged into practices, policies, and programs. This information delivery is often targeted at providers in public health and clinical settings and policymakers to improve public health decision-making. Here, we use provider to refer to a person or group that provides something—in this case, information. The chasm between how evidence-based information is disseminated and how this information is used by providers and policymakers is well-documented [ 1 ] and further evidenced by the ongoing COVID-19 pandemic [ 2 , 3 ].

The definition of dissemination research has been modified over the years and is not consistent across various sources. Dissemination research could be advanced by further development of existing conceptual and theoretical work. In a previous review [ 4 ], nine D&I science frameworks were categorized as “dissemination only” frameworks (i.e., the explicit focus of the framework was on the spread of information about evidence-based interventions to a target audience) [ 4 ]. Frameworks are important because they provide a systematic way to develop, plan, manage and evaluate a study [ 5 , 6 ]. The extent to which dissemination scholars are using frameworks to inform their studies, and which frameworks are used, is unclear.

Building on previous compilations of dissemination frameworks [ 7 ], this paper intends to advance the knowledge of dissemination research by examining dissemination frameworks reported in the empirical literature, cataloging the constructs across different frameworks, and providing definitions for these constructs. A scoping review is ideal at this stage of the dissemination research literature because it helps map the existing frameworks from a body of emerging literature and identifies gaps in the field [ 8 ].

Specifically, this study has three aims: (1) to conduct a scoping review of the empirical dissemination literature and identify the dissemination frameworks informing those studies, (2) to examine how scholars define dissemination, and (3) to catalog and define the constructs from the dissemination frameworks identified in aim 1 and the frameworks categorized as dissemination only by Tabak et al. [ 4 ]

The methods section is divided into the three aims of this study. First, we report the methods for our scoping review to identify the frameworks used in empirical dissemination studies. Second, we report on how we identified the definitions of dissemination. Third, we report the methods for abstracting the dissemination constructs from the frameworks identified in the empirical literature (aim 1) and from the frameworks categorized as “dissemination only” by Tabak et al. [ 4 ] Tabak et al. [ 4 ] categorized models “on a continuum from dissemination to implementation” and acknowledge that “these divisions are intended to assist the reader in model selection, rather than to provide actual classifications for models.” For the current review, we selected only those categorized as dissemination-only because we aimed to examine whether there were any distinct components between the dissemination and implementation frameworks by coding the dissemination-only frameworks.

Scoping review of the literature

We conducted a scoping review to identify dissemination frameworks used in the empirical dissemination literature. A scoping review is appropriate as the goal of this work is to map the current state of the literature, not to evaluate evidence or provide specific recommendations as is the case with a systematic review [ 8 ]. We followed the method developed by Arksey and O’Malley [ 9 ] and later modified by Levac and colleagues [ 10 ]. In doing so, we first identified the research questions (i.e., “Which dissemination frameworks are used in the literature?” and “How are the dissemination constructs defined?”), identified relevant studies (see below), and charted the data to present a summary of our results.

We iteratively created a search strategy in Scopus with terms relevant to dissemination. We ran the search in 2017 and again in December 2020, using the following terms: TITLE-ABS-KEY (dissem* OR (knowledge AND trans*) OR diffuse* OR spread*) in the 20 most relevant journals for the D&I science field, identified by Norton et al. [ 11 ] We ran an identical search at a second time point due to several logistical reasons. This review was an unfunded project conducted by faculty and students who experienced numerous significant life transitions during the project period. We anticipated the original search would be out of date by the time of submission for publication, thus wanted to provide the most up-to-date literature feasible given the time needed to complete the review steps. This approach is appropriate for systematic and scoping reviews [ 12 ]

We included studies if they were (a) quantitative or mixed methods empirical studies, (b) if they were about the dissemination of information (e.g., guidelines) to targeted professional audiences, and (c) published since 1985. Articles were excluded if they were (a) systematic reviews, commentaries, or other non-empirical articles; (b) qualitative studies; (c) scale-up studies (i.e., expanding a program into additional delivery settings); (d) case studies or description of programs; and/or (e) dissemination of information to lay consumer audiences or the general public. Some of the exclusion criteria, specifically around distinguishing studies that were dissemination studies from scale-up or health communication studies, were refined as we reviewed the paper abstracts. In the “Definition of dissemination section, we explain our rationale and process to distinguish these types of studies.

The screening procedures were piloted among all coders with a random sample of articles. AB, SaM, CH, CG, EK, and CWB screened titles for inclusion/exclusion independently, then met to ensure a shared understanding of the criteria and to generate consensus. The same coders then reviewed titles based on the above inclusion/exclusion criteria. Any unclear records were retained for abstract review. Consistent with the previously utilized methodology, the abstract review was conducted sequentially to the title review [ 13 , 14 ]. This approach can improve efficiency while maintaining accuracy [ 15 ]. In this round of review, abstracts were single-screened for inclusion/exclusion. Then, 26% of the articles were independently co-screened by pairs of coders; coding pairs met to generate consensus on disagreements.

Articles that passed to full-text review were independently screened by two coders (AB, CH, EK, and CWB). Coders met to reach a consensus and a third reviewer was consulted if the pair could not reach an agreement. From included records, coders extracted bibliometric information about the article (authors, journal, and year of publication) and the name of the framework used in the study (if a framework was used). Coders met regularly to discuss any discrepancies in coding and to generate consensus; final decisions were made by a third reviewer if necessary.

Review of definitions of dissemination

First, we compiled the list of frameworks identified in the empirical studies. Because some frameworks categorized as dissemination-only by the review of frameworks in Tabak et al. [ 4 ] were not present in our sample, we added those to our list of frameworks to review. From the articles describing these frameworks, we extracted dissemination definitions, constructs, and construct definitions. AB, SM, AM, and MP independently abstracted and compared the constructs’ definitions.

Review of dissemination constructs

Once constructs were identified, the frequency of the constructs was counted, and definitions were abstracted. We then organized the constructs into four categories: dissemination processes, determinants, strategies, and outcomes. These categories were organized based on themes by AB and reviewed by all authors. We presented different versions of these categories to groups of stakeholders along our process, including posters at the 2019 and 2021 Conferences on the Science of Dissemination and Implementation in Health, the Washington University Network for Dissemination and Implementation Researchers (WUNDIR), and our network of D&I research peers. During these presentations and among our internal authorship group, we received feedback that the categorization of the constructs was helpful.

We defined the constructs in the dissemination process as constructs that relate to processes, stages, or events by which the dissemination process happens. The dissemination determinants construct encompasses constructs that may facilitate or obstruct the dissemination process (i.e., barriers or facilitators). The dissemination strategies constructs are those that describe the approaches or actions of a dissemination process. Finally, dissemination outcomes are the identified dissemination outcomes in the frameworks (distinct from health service, clinical, or population health outcomes). These categories are subjective and defined by the study team. The tables in Additional file 1 include our suggested labels and definitions for the constructs within these four categories, the definitions as provided by the articles describing the frameworks, and the total frequency of each construct from the frameworks reviewed.

The PRISMA Extension for Scoping Reviews (PRISMA-ScR) flowchart is shown in Fig. ​ Fig.1. 1 . The combined searches yielded 6017 unique articles. Of those, 5622 were excluded during the title and abstract screening. Of the 395 full-text articles, we retained 89 in our final sample.

PRISMA chart

Papers were excluded during the full-text review for several reasons. Many papers ( n = 101, 33%) were excluded because they did not meet the coding definitions for dissemination studies. For example, some studies were focused on larger quality improvement initiatives without a clear dissemination component while other studies reported disseminating findings tangentially. Many ( n = 61; 20%) were excluded because they reported a study testing approaches to spread information to the general public or lay audiences instead of to a group of professionals (e.g., disseminating information about HIV perinatal transmission to mothers, not healthcare providers.) Several articles ( n = 55, 18%) were related to the scale up of interventions and not the dissemination of information.

Frameworks identified

Table ​ Table1 1 shows the frameworks used in the included studies. We identified a total of 27 unique frameworks in the empirical studies. Out of the 27 frameworks identified, only three overlapped with the 11 frameworks cataloged as “dissemination only” in Tabak et al. [ 4 ] review. Two frameworks identified in the empirical studies were cataloged by Tabak et al [ 4 ] as “D = I,” one was cataloged as “D > I,” and one as “I only.” Additional file 1 : Table S1 shows all the frameworks, with frameworks 1–11 being “D only” from Tabak et al. [ 4 ], and frameworks 12–34 are the ones identified in our empirical sample. Rogers’ diffusion of innovation [ 16 ] was used most frequently (in 10 studies), followed by the Knowledge to Action Framework (in 4 studies) [ 17 ] and RE-AIM (in 3 studies) [ 18 ]. Dobbins’ Framework for the Dissemination and Utilization of Research for Health-Care Policy and Practice [ 19 ], the Interactive Systems Framework and Network Theory [ 20 ], and Kingdon’s Multiple Streams Framework [ 21 ] were each used by two studies. Thirty studies (33%) did not explicitly describe a dissemination framework that informed their work.

Frequency of frameworks used in the dissemination studies from our sample ( N = 89)

a Identified as D only framework in Tabak et al.

b Identified as D = I in Tabak et al.

c Identified as D > I in Tabak et al.

d Identified as I only at Tabak et al. Frameworks with no note were not identified in Tabak et al.

Definition of dissemination

Table ​ Table2 2 shows the definition of dissemination from the frameworks. Out of the 38 frameworks, only 12 (32%) defined dissemination. There is wide variability in the depth of the definitions, with some authors defining dissemination as a process “transferring research to the users,” [ 24 ] and others defining it as both a process and an outcome [ 19 , 23 ]. The definitions of dissemination varied among the 13 frameworks that defined dissemination; however, some shared characteristics were identified. In nine of the 13 frameworks, the definition of dissemination included language about the movement or spread of something, whether an idea, innovation, program, or research finding [ 16 , 23 – 28 , 31 , 32 ]. Seven of the frameworks described dissemination as active, intentional, or planned by those leading a dissemination effort [ 7 , 16 , 23 , 25 – 27 , 32 ]. Five frameworks specified some type of outcome as a result of dissemination (e.g., the adoption of an innovation or awareness of research results) [ 7 , 19 , 23 , 27 , 29 , 30 ]. Three of the frameworks’ definitions included the role of influential determinants of dissemination [ 19 , 27 , 29 , 30 ]. Only two frameworks highlighted dissemination as a process [ 23 , 25 ].

Definition of dissemination across the dissemination frameworks

Definition of dissemination constructs

Below, we describe the results presented in Tables ​ Tables3, 3 , ​ ,4, 4 , ​ ,5, 5 , and ​ and6 6 with constructs grouped by dissemination process, determinants, strategies, and outcomes. The definitions proposed for the constructs were based on a thematic review of the definitions provided in the articles, which can be found in the Additional file 1 : Tables S2-S5.

Dissemination process constructs, suggested definition, frequency of construct across frameworks, and other names in the literature

Dissemination determinants constructs, suggested definition, frequency of construct across frameworks, and other names in the literature

Dissemination strategy constructs, suggested definition, frequency of construct across frameworks, and other names in the literature

Dissemination outcome constructs, suggested definition, frequency of construct across frameworks, and other names in the literature

Table ​ Table3 3 shows the constructs that relate to the dissemination processes , i.e., the steps or processes through which dissemination happens. Seven constructs were categorized as processes: knowledge inquiry, knowledge synthesis, communication, interaction, persuasion, activation, and research transfer. That is, six frameworks suggest that the dissemination process starts with an inquiry of what type of information is needed to close the knowledge gap. Next, there is a process of gathering and synthesizing the information, including examining the context in which the information will be shared. After the information is identified and gathered, there is a process of communication, interaction with the information, and persuasion where the information is shared with the target users, where the users then engage with the information and activate towards action based on the information received. Finally, there is a process of research transfer, where the information sharing “becomes essentially independent of explicit intentional change activity.” [ 33 ]

Table ​ Table4 4 shows the 17 constructs categorized as dissemination determinants , which are constructs that reflect aspects that may facilitate or hinder the dissemination process. Determinants identified included content of the information, context, interpersonal networks, source of knowledge and audience, the medium of dissemination, opinion leaders, compatibility of the information with the setting, type of information, and capacity of the audience to adopt the innovation. Communication, the salience of communication, and users’ perceived attitudes towards the information were the most frequent constructs ( n = 14 each), followed by context ( n = 13), interpersonal networks ( n = 12), sources of knowledge, and audience ( n = 10 each).

Table ​ Table5 5 shows the nine constructs related to dissemination strategies , which are constructs that describe the approaches or actions to promote or support dissemination. Leeman and colleagues [ 34 ] conceptualize dissemination strategies as strategies that provide synthesis, translation, and support of information. The authors refer to dissemination as two broad strategies: developing materials and distributing materials. We identified several strategies related to the synthesis of information (e.g., identify the knowledge), translation of information (e.g., adapt information to context), and other constructs. Monitoring and evaluation were the most frequent constructs ( n = 10), with identify the quality gap and increase audience’s skills next ( n = 6).

Finally, Table ​ Table6 6 shows the dissemination outcomes , which are constructs related to the effects of the dissemination process. Fifteen constructs were categorized here, including awareness and changes in policy, decision and impact, adoption and cost, emotion reactions, knowledge gained, accountability, maintenance, persuasion, reception, confirmation, and fidelity. Knowledge utilization was the most cited construct across frameworks ( n = 11), followed by awareness and change in policy ( n = 8 each).

The goals of this study were threefold. First, we conducted a scoping review of the empirical literature to catalog the dissemination frameworks informing dissemination studies. Second, we compiled the definition of dissemination, and third, we cataloged and defined the constructs from the dissemination frameworks. During our review process, we found that clearly identifying dissemination studies was more complicated than anticipated. Defining the sample of articles to code for this study was a challenge because of the large variability of studies that use the word “dissemination” in the titles but that are actually scale-up or health communication studies.

The high variability in the definition of dissemination poses a challenge for the field because if we do not clearly define what we are doing, we are unable to set boundaries to distinguish dissemination research from other fields. Among the identified frameworks that defined dissemination, the definitions highlighted that dissemination involved the spread of something, whether knowledge, an innovation, or a program. Distinct from diffusion, several definitions described dissemination as an active process, using intentional strategies. Few definitions described the role of determinants, whether dissemination is a process or a discrete event, and what strategies and outcomes may be pertinent. Future work is needed to unify these distinct conceptualizations into a comprehensive definition that dissemination researchers can use.

While it is clear that dissemination differs from diffusion, as the latter has been considered the passive and “haphazard” spread of information [ 35 ], the distinction between dissemination and scale-up—as shown in the definitions identified in this study—is less clear. Some articles from our search not included in the review conceptualized dissemination as similar to scale-up. To clarify the distinction between dissemination and scale-up in our review, we used the WHO’s definition of scale-up [ 36 ] as “deliberate efforts to increase the impact of successfully tested health innovations to benefit more people and to foster policy and program development on a lasting basis.” In other words, based on these definitions, our team considered scale-up as referencing active efforts to spread evidence-based interventions , whereas diffusion is the passive spread of information. Dissemination, therefore, can be conceptualized as the active and planned spread of information.

Another helpful component in distinguishing dissemination science from other sciences is related to the target audience. Brownson et al. [ 1 ] define dissemination as an “active approach of spreading evidence-based information to the target audience via determined channels using planned strategies” (p. 9). Defining the target audience in the context of dissemination is important because it may help distinguish the field from social marketing. Indeed, several studies we excluded involved sharing information with the public (e.g., increasing the awareness of the importance of sunscreen in public swimming pools). Grier and Bryant define social marketing as a “program-planning process that applies commercial marketing concepts and techniques to promote voluntary behavior change ( … ) by groups of individuals, often referred to as the target audience.” [ 37 ] The target audience in the context of social marketing, the authors explain, is usually considered consumers but can also be policymakers [ 37 ]. To attempt to delineate a distinction between these two fields, dissemination work has traditionally identified professionals (e.g., clinicians, public health practitioners, policymakers) as the target audience of dissemination efforts, whereas the target audience in social marketing is conceptualized as a broader audience. Figure ​ Figure2 2 shows how we conceptualize the distinct components of dissemination research from other fields. Based on these distinctions, we propose the following coalesced definition for dissemination research to guide this review: the scientific study of the targeted distribution of information to a specific professional person or group of professionals. Clearly distinguishing dissemination from scale-up as well as health communication will help further advance the dissemination research field.

Proposed distinction of definitions between diffusion, scale-up, and dissemination

Our results show that of the empirical papers identified in this review, 51% used a framework to guide their study. This finding mirrors the suboptimal use of frameworks in the field of implementation research [ 38 , 39 ], with scholars recently putting forth guidance on how to select and use frameworks to enhance their use in implementation research studies [ 6 ]. Similarly, we provide a catalog of dissemination frameworks and their constructs identified in dissemination studies. It is necessary to move the dissemination research field forward by embedding frameworks in dissemination-focused studies.

Some empirical papers included in our review used frameworks based on the knowledge translation literature. Knowledge translation, a field most prominent outside the USA, has been defined as “a dynamic and iterative process that includes the synthesis, dissemination, exchange and ethically sound application of knowledge to improve health, provide more effective health services and products, and strengthen the health care system” [ 40 ]. As such, it conceptualizes an interactive relationship between the creation and the application of knowledge. In the USA, however, researchers tend to conceptualize dissemination as a concept discrete from implementation and use the acronym “D&I” to identify these two fields.

While one could state that there is a distinct set of outcomes, methods, and frameworks between dissemination and implementation fields, previous scholars have cataloged [ 4 ] a continuum, from dissemination only” to “implementation only” frameworks. Consistent with this, our findings show that scholars have adapted implementation frameworks to fit dissemination outcomes (e.g., Klesges’ adaptation of RE-AIM [ 41 ]), while other frameworks have both dissemination and implementation components (e.g., integrated Promoting Action on Research Implementation in Health Services [i-PARIHS] [ 42 ]). Additionally, behavioral change frameworks (e.g., theory of planned behavior) were cataloged in our study as they were used in included articles. The use of implementation frameworks in studies identified here as dissemination studies highlights at least three potential hypotheses. One possibility is the use of implementation frameworks in dissemination studies is due to the underdevelopment of the field of dissemination, as shown in the challenges that we found in the conceptual definition of dissemination. We hope that, by clearly outlining a definition of dissemination, scholars can start to empirically examine whether there are distinct components between implementation and dissemination outcomes and processes.

The second hypothesis is that we still do not have enough evidence in the dissemination or implementation fields to be dogmatic about the categorization of frameworks as either “dissemination” or “implementation.” Until we have more robust evidence about what is and what is not dissemination (or other continua along which frameworks may be categorized), we caution against holding too firm to characterizations of frameworks [ 38 , 43 – 45 ] Frameworks evolve as more empirical evidence is gathered [ 43 , 45 – 49 ], and they are applied in different settings and contexts. We could hypothesize that it is less important, as of now, to categorize a framework as an implementation or dissemination framework and instead clearly explain why a specific framework was selected and how it is applied in the study.

Selection and application of frameworks in dissemination and implementation research is still a challenge, especially considering scholars may often select frameworks in a haphazard way [ 6 , 50 , 51 ]. While scholars have put forward some guidance to select implementation frameworks [ 6 , 52 ], the challenge in the dissemination and implementation research fields is likely not only in the selection of the frameworks but perhaps more so in the misuse or misapplication of frameworks, theories, or models. A survey indicated that there is little consensus on the process that scholars use to select frameworks and that scholars select frameworks based on several criteria, including familiarity with the framework [ 50 ]. As such, Birken et al. [ 52 ] offer other criteria for the selection of frameworks, such as (a) usability (i.e., whether the framework includes relevant constructs and whether the framework provides an explanation of how constructs influence each other), (b) applicability (i.e., how a method, such as an interview, can be used with the framework; whether the framework is generalizable to different contexts), and (c) testability (i.e., whether the framework proposed a testable hypothesis and whether it contributes to an evidence-based or theory development). Moullin et al. [ 6 ] suggest that implementation frameworks should be selected based on their (a) purpose, (b), levels of analysis (e.g., provider, organizational, system), (c) degree of inclusion and depth of analysis or operationalization of implementation concepts, and (d) the framework’s orientation (e.g., setting and type of intervention).

More than one framework can be selected in one study, depending on the research question(s). The application of a framework can support a project in the planning stages (e.g., examining the determinants of a context, engaging with stakeholders), during the project (e.g., making explicit the mechanisms of action, tracking and exploring the process of change), and after the project is completed (e.g., use of the framework to report outcomes, to understand what happened and why) [ 6 , 51 , 53 ]. We believe that similar guidance can and should be applied to dissemination frameworks; further empirical work may be needed to help identify how to select and apply dissemination and/or implementation frameworks in dissemination research. The goal of this review is to support the advancement of the dissemination and implementation sciences by identifying constructs and frameworks that scholars can apply in their dissemination studies. Additional file 1 : Tables S6-S9 show the frequency of constructs per framework, and readers can see the variability in the frequency of constructs per framework to help in their selection of frameworks.

A third hypothesis is that the processes of dissemination and implementation are interrelated, may occur simultaneously, and perhaps support each other in the uptake of evidence-based interventions. For example, Leppin et al. [ 54 ] use the definition of implementation based on the National Institutes of Health: “the adoption and integration of evidence-based health interventions into clinical and community settings for the purposes of improving care delivery and efficiency, patient outcomes, and individual and population health” [ 55 ], and implementation research as the study of this process to develop a knowledge base about how interventions can be embedded in practices. In this sense, implementation aims to examine the “how” to normalize interventions in practices, to enhance uptake of these interventions, guidelines, or policies, whereas dissemination examines how to spread the information about these interventions, policies, and practices, intending to support their adoption (see Fig. ​ Fig.1). 1 ). In other words, using Curran’s [ 56 ] simple terms, implementation is about adopting and maintaining “the thing” whereas dissemination is about intentionally spreading information to enable learning about “the thing.” As Leppin et al. argue, these two sciences [ 54 , 57 , 58 ], while separate, could co-occur in the process of supporting the uptake of evidence-based interventions. Future work may entail empirically understanding the role of these frameworks in dissemination research.

This review aimed to advance a critical step in the dissemination literature by defining and categorizing dissemination constructs. Constructs are subjective, socially constructed concepts [ 59 ], and therefore their definitions may be bounded by factors including, but not limited to, the researchers’ discipline and background, the research context, and time [ 60 ]. This is evident in the constructs’ lack of consistent, clear definitions (see Additional file 1 ). The inconsistency in the definitions of the constructs is problematic because it impairs measurement development and consequently validity and comparability across studies. The lack of clear definitions of the dissemination constructs may be due to the multidisciplinary nature of the D&I research field in general [ 61 , 62 ], which is a value of the field. However, not having consistency in terms and definitions makes it difficult to develop generalizable conclusions and synthesize scientific findings regarding dissemination research.

We identified a total of 48 constructs, which we separated into four categories: dissemination processes, determinants, strategies, and outcomes. By providing these categories, we can hope to help advance the field of dissemination research to ensure rigor and consistency. Process constructs are important to guide the critical steps and structure that scholars may need to take when doing dissemination research. Of note is that the processes identified in this study may not be unique to dissemination research but rather to the research process in general. As the field of dissemination research advances, it will be interesting to examine whether there are unique components in these process stages that are unique to the dissemination field. In addition to the process, an examination of dissemination determinants (i.e., barriers and facilitators) is essential in understanding how contextual factors occurring at different levels (e.g., information recipient, organizational setting, policy environment) influence dissemination efforts and impede or improve dissemination success [ 7 ]. Understanding the essential determinants will help to guide the selection and design of strategies that can support dissemination efforts. Finally, the constructs in the dissemination outcomes will help examine levels and processes to assess.

The categorization of the constructs was not without challenges. For example, persuasion was coded as a strategy (persuading) and as an outcome (persuasion). Likewise, the construct confirmation could be conceptualized as a stage [ 16 ] or as an outcome [ 19 ]. The constructs identified in this review provide an initial taxonomy for understanding and assessing dissemination outcomes, but more research and conceptualization are needed to fully describe dissemination processes, determinants, strategies, and outcomes. Given the recent interest in the dissemination literature [ 22 , 63 ], a future step for the field is examining the precise and coherent definition and operationalization of dissemination constructs, along with the identification or development of measures to assess them.

Limitations

A few limitations to this study should be noted. First, the search was limited to one bibliometric database and from journals publishing D&I in health studies. We limited our search to one database because we aimed to capture articles from Norton et al. [ 11 ], and therefore, our search methodology was focused on journals instead of on databases. Future work learning from other fields, and doing a broader search on other databases could provide different perspectives. Second, we did not include terms such as research utilization, research translation, knowledge exchange, knowledge mobilization, or translation science in our search, limiting the scope and potential generalizability of our search. Translation science has been defined as being a different science than dissemination, however. Leppin et al. [ 54 ], for example, offer the definition of translation science as the science that aims to identify and advance generalizable principles to expedite research translation, or the “process of turning observations into interventions that improve health” (see Fig. ​ Fig.1). 1 ). Translation research, therefore, focuses on the determinants to achieve this end. Accordingly, Wilson et al. [ 7 ] used other terms in their search, including translation, diffusion of innovation, and knowledge mobilization and found different frameworks in their review. In their paper, Wilson and colleagues [ 7 ] provided a different analysis than ours in that they aimed to examine the theoretical underpinning of the frameworks identified by them. Our study is different from theirs in that we offer the definition of disseminating and a compilation of constructs and their definitions. A future study could combine the frameworks identified by our study with the ones identified by Wilson and colleagues and detail the theoretical origins of the frameworks, and the definitions of the constructs to support in the selection of frameworks for dissemination studies. Third, by being stringent in our inclusion criteria, we may have missed important work. Several articles were excluded from our scoping review because they were examining the spread of an evidence-based intervention (scale up) or of the spread of dissemination for the public (health communication). As noted above, however, clearly distinguishing dissemination from scale-up and from health communication will help further advance the dissemination research field and identify its mechanisms of action. Fourth, given the broad literatures in diffusion, dissemination, and social marketing, researchers may disagree with our definitions and how we conceptualized the constructs. Fifth, we did not code qualitative studies because we wanted to have boundaries in this study as it is a scoping study. Future studies could examine the application of frameworks in qualitative work. It is our hope that future research can build from this work to continue to define and test the dissemination constructs.

Conclusions

Based on the review of frameworks and the empirical literature, we defined dissemination research and outlined key constructs in the categories of dissemination process, strategies, determinants, strategies, and outcomes. Our data indicate that the field of dissemination research could be advanced with a more explicit focus on methods and a common understanding of constructs. We hope that our review will help guide the field in providing a narrative taxonomy of dissemination constructs that promote clarity and advance the dissemination research field. We hope that future stages of the dissemination research field can examine specific measures and empirically test the mechanisms of action of the dissemination process.

Acknowledgements

The authors would like to thank the WUNDIR community for their invaluable feedback.

Abbreviations

Authors’ contributions.

AB developed the research question. AB, CH, AM, CAG, SM, and RT designed the study. AB, CH, EK, ABM, CAG, SaM, CWB, MP, RGT, and SM coded the data. BS supported with editing and references. All authors collaborated on writing the manuscript, and all approve the final version of the document.

AB is supported by the National Heart, Lung and Blook Institute U24HL154426 and 5U01HL133994, the National Institute of Child Health and Human Development R01HD091218, and the National Institute of Mental Health P50MH122351. CWB is funded by NIMHD T37 {"type":"entrez-nucleotide","attrs":{"text":"MD014218","term_id":"1884918698","term_text":"MD014218"}} MD014218 . MP is supported by the National Institute of Allergy and Infectious Diseases (K24AI134413). ABM is supported by the National Heart, Lung, and Blood Institute (1T32HL130357). RGT and SM were supported by the National Institute of Diabetes and Digestive and Kidney Diseases P30DK092950 and by the Nutrition Obesity Research Center, P30 DK056341, and Cooperative Agreement number U48DP006395 from the Centers for Disease Control and Prevention. AB, SM, and TB were supported by the National Cancer Institute P50CA244431. AAB, ABM, MP, RGT, and SM were also supported by the National Center for Advancing Translational Sciences UL1TR002345. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official positions of the National Institutes of Health or the Centers for Disease Control and Prevention.

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Declarations.

Not applicable

The authors declare that they have no competing interests.

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Framework Analysis – Method, Types and Examples

Table of Contents

Framework Analysis

Definition:

Framework Analysis is a qualitative research method that involves organizing and analyzing data using a predefined analytical framework. The analytical framework is a set of predetermined themes or categories that are derived from the research questions or objectives. The framework provides a structured approach to data analysis and can help to identify patterns, themes, and relationships in the data.

Steps in Framework Analysis

Here are the general steps in Framework Analysis:

Familiarization

Get familiar with the data by reading and re-reading it. This step helps you to become immersed in the data and to get a sense of its content, structure, and scope.

Identify a Coding Framework

Identify a coding framework or set of themes that will be used to analyze the data. These themes can be derived from existing literature or theories or developed based on the data itself.

Code the data by applying the coding framework to the data. This involves breaking down the data into smaller units and assigning each unit to a particular theme or category.

Chart or summarize the data by creating tables or matrices that display the distribution and frequency of each theme or category across the data set.

Mapping and interpretation

Analyze the data by examining the relationship between different themes or categories, and by exploring the implications and meanings of the findings in relation to the research question.

Verification

Verify the accuracy and validity of the findings by checking them against the original data, comparing them with other sources of data, and seeking feedback from others.

Report the findings by presenting them in a clear, concise, and organized manner. This involves summarizing the key themes, presenting supporting evidence, and providing interpretations and recommendations based on the findings.

Framework Analysis Conducting Guide

Here is a step-by-step guide to conducting framework analysis:

• Define the research question: The first step in conducting framework analysis is to clearly define the research question or objective that you want to investigate.
• Develop the analytical framework: Develop a coding framework or a set of predetermined themes or categories that are relevant to the research question. These themes or categories can be derived from existing literature or theories, or they can be developed based on the data collected.
• Data collection: Collect the data using a suitable method such as interviews, focus groups, surveys or observation.
• Familiarization: Transcribe and familiarize yourself with the data. Read through the data several times and take notes to identify any patterns, themes or issues that are emerging.
• Coding : Code the data by identifying key themes or categories and assigning each piece of information to a specific theme or category.
• Charting: Create charts or tables that display the frequency and distribution of each theme or category. This helps to summarize the data and identify patterns.
• Mapping and interpretation: Analyze the data to identify patterns, relationships, and themes. Interpret the findings in light of the research objectives and provide explanations for any significant patterns or themes that have emerged.
• Validation : Validate the findings by sharing them with others and seeking feedback. This can help to ensure that the findings are robust and reliable.
• Report writing: Write a report that summarizes the findings, includes quotes or examples from the data to support the findings and provides recommendations for future research.

Applications of Framework Analysis

Framework Analysis has a wide range of applications in research, including:

• Policy analysis: Framework Analysis can be used to analyze policies and policy documents to identify key themes, patterns, and underlying assumptions.
• Social science research: Framework Analysis is commonly used in social science research to analyze qualitative data from interviews, focus groups, and other sources.
• Health research: Framework Analysis can be used to analyze qualitative data from health research studies, such as patient and provider perspectives, to identify themes and patterns.
• Environmental research : Framework Analysis can be used to analyze qualitative data from environmental research studies to identify themes and patterns related to environmental attitudes, behaviors, and practices.
• Education research: Framework Analysis can be used to analyze qualitative data from educational research studies to identify themes and patterns related to teaching practices, student learning, and educational policies.
• Market research: Framework Analysis can be used to analyze qualitative data from market research studies to identify themes and patterns related to consumer attitudes, behaviors, and preferences.

Examples of Framework Analysis

Here are some examples of Framework Analysis in various research contexts:

• Health Research: A study on the experiences of cancer survivors might use Framework Analysis to identify themes related to the psychological, social, and physical aspects of survivorship. Themes might include coping strategies, social support, and health outcomes.
• Education Research: A study on the impact of a new teaching approach might use Framework Analysis to identify themes related to the implementation of the approach, the effectiveness of the approach, and barriers to its implementation. Themes might include teacher attitudes, student engagement, and logistical challenges.
• Environmental Research : A study on the factors that influence pro-environmental behaviors might use Framework Analysis to identify themes related to environmental attitudes, behaviors, and practices. Themes might include social norms, personal values, and perceived barriers to behavior change.
• Policy Analysis: A study on the implementation of a new policy might use Framework Analysis to identify themes related to policy development, implementation, and outcomes. Themes might include stakeholder perspectives, organizational structures, and policy effectiveness.
• Social Science Research: A study on the experiences of immigrant families might use Framework Analysis to identify themes related to the challenges and opportunities faced by immigrant families in their new country. Themes might include language barriers, cultural differences, and social support.

When to use Framework Analysis

Framework Analysis is a useful method for analyzing qualitative data when the research questions require an in-depth exploration of a particular phenomenon, concept, or experience. It is particularly useful when:

• The research involves multiple sources of qualitative data, such as interviews, focus groups, or documents, that need to be analyzed and compared.
• The research questions require a systematic and structured approach to data analysis that enables the identification of patterns, themes, and relationships in the data.
• The research involves a large and complex dataset that requires a method for organizing and synthesizing the data in a meaningful way.
• The research aims to generate new insights and understandings from the data, rather than testing pre-existing hypotheses or theories.
• The research requires a method that is transparent, replicable, and verifiable, as Framework Analysis provides a clear framework for data analysis and reporting.

Purpose of Framework Analysis

The purpose of Framework Analysis is to systematically organize and analyze qualitative data in a structured and transparent manner. The method is designed to identify patterns, themes, and relationships in the data that are relevant to the research question or objective. By using a rigorous and transparent approach to data analysis, Framework Analysis enables researchers to generate new insights and understandings from the data, and to provide a clear and structured presentation of the findings.

The method is particularly useful for analyzing large and complex qualitative datasets that require a method for organizing and synthesizing the data in a meaningful way. It can be used to explore a wide range of research questions and objectives across various fields, including health research, social science research, education research, policy analysis, and environmental research, among others.

Overall, the purpose of Framework Analysis is to provide a systematic and transparent method for analyzing qualitative data that enables researchers to generate new insights and understandings from the data in a rigorous and structured manner.

Characteristics of Framework Analysis

Some Characteristics of Framework Analysis are:

• Systematic and Structured Approach: Framework Analysis provides a systematic and structured approach to data analysis that involves a series of steps that are followed in a predetermined order.
• Transparency and Replicability: Framework Analysis emphasizes transparency and replicability, as it involves a clearly defined process for data analysis that can be applied consistently across different datasets and research questions.
• Flexibility : Framework Analysis is flexible and adaptable to a wide range of research contexts and objectives, as it can be used to analyze qualitative data from various sources and to explore different research questions.
• In-depth Exploration of the Data: Framework Analysis enables an in-depth exploration of the data, as it involves a thorough and detailed analysis of the data to identify patterns, themes, and relationships.
• Applicable to Large and Complex Datasets: Framework Analysis is particularly useful for analyzing large and complex qualitative datasets, as it provides a method for organizing and synthesizing the data in a meaningful way.
• Data-Driven: Framework Analysis is data-driven, as it focuses on the analysis and interpretation of the data rather than on pre-existing hypotheses or theories.
• Emphasis on Contextual Understanding : Framework Analysis emphasizes contextual understanding, as it involves a detailed examination of the data to identify the social, cultural, and environmental factors that may influence the phenomena under investigation.

Advantages of Framework Analysis

Some Advantages of Framework Analysis are as follows:

• Transparency : Framework Analysis provides a clear and structured approach to data analysis, which makes the process transparent and easy to follow. This ensures that the findings can be easily replicated or verified by other researchers.
• Thorough Analysis : Framework Analysis enables a thorough and detailed analysis of the data, which allows for the identification of patterns, themes, and relationships that may not be apparent through other methods.
• Contextual Understanding: Framework Analysis emphasizes the importance of understanding the context in which the data was collected, which enables a more nuanced interpretation of the findings.
• Collaborative Analysis: Framework Analysis can be used as a collaborative method for data analysis, as it allows multiple researchers to work together to analyze the data and develop a shared understanding of the findings.
• Efficient and Time-saving: Framework Analysis can be an efficient and time-saving method for analyzing qualitative data, as it provides a structured and organized approach to data analysis that can help researchers manage and synthesize large datasets.
• Comprehensive Reporting: Framework Analysis can help ensure that the research findings are comprehensive and well-reported, as the method provides a clear framework for presenting the results.

Limitations of Framework Analysis

Some Limitations of Framework Analysis are as follows:

• Subjectivity : Framework Analysis relies on the interpretation of the researchers, which can introduce subjectivity into the analysis process.
• Time-consuming : Framework Analysis can be a time-consuming method for data analysis, particularly when working with large and complex datasets.
• Limited ability to generate new theory : Framework Analysis is a deductive approach that relies on pre-existing theories and concepts to guide the analysis, which may limit the ability to generate new theoretical insights.
• Risk of oversimplification: The structured approach of Framework Analysis can lead to oversimplification of the data, as complex issues may be reduced to predefined categories or themes.
• Limited ability to capture the complexity of the data : The predefined categories or themes used in Framework Analysis may not be able to capture the full complexity of the data, particularly when dealing with nuanced or context-specific phenomena.
• Limited use with non-textual data : Framework Analysis is primarily designed for analyzing qualitative textual data and may not be as effective for analyzing non-textual data such as images, videos, or audio recordings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Objectives: The Compass of Your Study

Table of contents

• 1 Definition and Purpose of Setting Clear Research Objectives
• 2 How Research Objectives Fit into the Overall Research Framework
• 3 Types of Research Objectives
• 4 Aligning Objectives with Research Questions and Hypotheses
• 5 Role of Research Objectives in Various Research Phases
• 6.1 Key characteristics of well-defined research objectives
• 6.2 Step-by-Step Guide on How to Formulate Both General and Specific Research Objectives
• 6.3 How to Know When Your Objectives Need Refinement
• 7 Research Objectives Examples in Different Fields
• 8 Conclusion

Embarking on a research journey without clear objectives is like navigating the sea without a compass. This article delves into the essence of establishing precise research objectives, serving as the guiding star for your scholarly exploration.

We will unfold the layers of how the objective of study not only defines the scope of your research but also directs every phase of the research process, from formulating research questions to interpreting research findings. By bridging theory with practical examples, we aim to illuminate the path to crafting effective research objectives that are both ambitious and attainable. Let’s chart the course to a successful research voyage, exploring the significance, types, and formulation of research paper objectives.

Definition and Purpose of Setting Clear Research Objectives

Defining the research objectives includes which two tasks? Research objectives are clear and concise statements that outline what you aim to achieve through your study. They are the foundation for determining your research scope, guiding your data collection methods, and shaping your analysis. The purpose of research proposal and setting clear objectives in it is to ensure that your research efforts are focused and efficient, and to provide a roadmap that keeps your study aligned with its intended outcomes.

To define the research objective at the outset, researchers can avoid the pitfalls of scope creep, where the study’s focus gradually broadens beyond its initial boundaries, leading to wasted resources and time. Clear objectives facilitate communication with stakeholders, such as funding bodies, academic supervisors, and the broader academic community, by succinctly conveying the study’s goals and significance. Furthermore, they help in the formulation of precise research questions and hypotheses, making the research process more systematic and organized. Yet, it is not always easy. For this reason, PapersOwl is always ready to help. Lastly, clear research objectives enable the researcher to critically assess the study’s progress and outcomes against predefined benchmarks, ensuring the research stays on track and delivers meaningful results.

How Research Objectives Fit into the Overall Research Framework

Research objectives are integral to the research framework as the nexus between the research problem, questions, and hypotheses. They translate the broad goals of your study into actionable steps, ensuring every aspect of your research is purposefully aligned towards addressing the research problem. This alignment helps in structuring the research design and methodology, ensuring that each component of the study is geared towards answering the core questions derived from the objectives. Creating such a difficult piece may take a lot of time. If you need it to be accurate yet fast delivered, consider getting professional research paper writing help whenever the time comes. It also aids in the identification and justification of the research methods and tools used for data collection and analysis, aligning them with the objectives to enhance the validity and reliability of the findings.

Furthermore, by setting clear objectives, researchers can more effectively evaluate the impact and significance of their work in contributing to existing knowledge. Additionally, research objectives guide literature review, enabling researchers to focus their examination on relevant studies and theoretical frameworks that directly inform their research goals.

Types of Research Objectives

In the landscape of research, setting objectives is akin to laying down the tracks for a train’s journey, guiding it towards its destination. Constructing these tracks involves defining two main types of objectives: general and specific. Each serves a unique purpose in guiding the research towards its ultimate goals, with general objectives providing the broad vision and specific objectives outlining the concrete steps needed to fulfill that vision. Together, they form a cohesive blueprint that directs the focus of the study, ensuring that every effort contributes meaningfully to the overarching research aims.

• General objectives articulate the overarching goals of your study. They are broad, setting the direction for your research without delving into specifics. These objectives capture what you wish to explore or contribute to existing knowledge.
• Specific objectives break down the general objectives into measurable outcomes. They are precise, detailing the steps needed to achieve the broader goals of your study. They often correspond to different aspects of your research question , ensuring a comprehensive approach to your study.

To illustrate, consider a research project on the impact of digital marketing on consumer behavior. A general objective might be “to explore the influence of digital marketing on consumer purchasing decisions.” Specific objectives could include “to assess the effectiveness of social media advertising in enhancing brand awareness” and “to evaluate the impact of email marketing on customer loyalty.”

Aligning Objectives with Research Questions and Hypotheses

The harmony between what research objectives should be, questions, and hypotheses is critical. Objectives define what you aim to achieve; research questions specify what you seek to understand, and hypotheses predict the expected outcomes.

This alignment ensures a coherent and focused research endeavor. Achieving it necessitates a thoughtful consideration of how each component interrelates, ensuring that the objectives are not only ambitious but also directly answerable through the research questions and testable via the hypotheses. This interconnectedness facilitates a streamlined approach to the research process, enabling researchers to systematically address each aspect of their study in a logical sequence. Moreover, it enhances the clarity and precision of the research, making it easier for peers and stakeholders to grasp the study’s direction and potential contributions.

Role of Research Objectives in Various Research Phases

Throughout the research process, objectives guide your choices and strategies – from selecting the appropriate research design and methods to analyzing data and interpreting results. They are the criteria against which you measure the success of your study. In the initial stages, research objectives inform the selection of a topic, helping to narrow down a broad area of interest into a focused question that can be explored in depth. During the methodology phase, they dictate the type of data needed and the best methods for obtaining that data, ensuring that every step taken is purposeful and aligned with the study’s goals. As the research progresses, objectives provide a framework for analyzing the collected data, guiding the researcher in identifying patterns, drawing conclusions, and making informed decisions.

Crafting Effective Research Objectives

The effective objective of research is pivotal in laying the groundwork for a successful investigation. These objectives clarify the focus of your study and determine its direction and scope. Ensuring that your objectives are well-defined and aligned with the SMART criteria is crucial for setting a strong foundation for your research.

Key characteristics of well-defined research objectives

Well-defined research objectives are characterized by the SMART criteria – Specific, Measurable, Achievable, Relevant, and Time-bound. Specific objectives clearly define what you plan to achieve, eliminating any ambiguity. Measurable objectives allow you to track progress and assess the outcome. Achievable objectives are realistic, considering the research sources and time available. Relevant objectives align with the broader goals of your field or research question. Finally, Time-bound objectives have a clear timeline for completion, adding urgency and a schedule to your work.

Step-by-Step Guide on How to Formulate Both General and Specific Research Objectives

So lets get to the part, how to write research objectives properly?

• Understand the issue or gap in existing knowledge your study aims to address.
• Gain insights into how similar challenges have been approached to refine your objectives.
• Articulate the broad goal of research based on your understanding of the problem.
• Detail the specific aspects of your research, ensuring they are actionable and measurable.

How to Know When Your Objectives Need Refinement

Your objectives of research may require refinement if they lack clarity, feasibility, or alignment with the research problem. If you find yourself struggling to design experiments or methods that directly address your objectives, or if the objectives seem too broad or not directly related to your research question, it’s likely time for refinement. Additionally, objectives in research proposal that do not facilitate a clear measurement of success indicate a need for a more precise definition. Refinement involves ensuring that each objective is specific, measurable, achievable, relevant, and time-bound, enhancing your research’s overall focus and impact.

Research Objectives Examples in Different Fields

The application of research objectives spans various academic disciplines, each with its unique focus and methodologies. To illustrate how the objectives of the study guide a research paper across different fields, here are some research objective examples:

• In Health Sciences , a research aim may be to “determine the efficacy of a new vaccine in reducing the incidence of a specific disease among a target population within one year.” This objective is specific (efficacy of a new vaccine), measurable (reduction in disease incidence), achievable (with the right study design and sample size), relevant (to public health), and time-bound (within one year).
• In Environmental Studies , the study objectives could be “to assess the impact of air pollution on urban biodiversity over a decade.” This reflects a commitment to understanding the long-term effects of human activities on urban ecosystems, emphasizing the need for sustainable urban planning.
• In Economics , an example objective of a study might be “to analyze the relationship between fiscal policies and unemployment rates in developing countries over the past twenty years.” This seeks to explore macroeconomic trends and inform policymaking, highlighting the role of economic research study in societal development.

These examples of research objectives describe the versatility and significance of research objectives in guiding scholarly inquiry across different domains. By setting clear, well-defined objectives, researchers can ensure their studies are focused and impactful and contribute valuable knowledge to their respective fields.

Defining research studies objectives and problem statement is not just a preliminary step, but a continuous guiding force throughout the research journey. These goals of research illuminate the path forward and ensure that every stride taken is meaningful and aligned with the ultimate goals of the inquiry. Whether through the meticulous application of the SMART criteria or the strategic alignment with research questions and hypotheses, the rigor in crafting and refining these objectives underscores the integrity and relevance of the research. As scholars venture into the vast terrains of knowledge, the clarity, and precision of their objectives serve as beacons of light, steering their explorations toward discoveries that advance academic discourse and resonate with the broader societal needs.

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• Open access
• Published: 26 April 2022

Definition and conceptualization of the patient-centered care pathway, a proposed integrative framework for consensus: a Concept analysis and systematic review

• Jean-Baptiste Gartner 1 , 2 , 3 , 4 , 5 ,
• Kassim Said Abasse 1 , 2 , 3 , 5 ,
• Frédéric Bergeron 6 ,
• Paolo Landa 3 , 7 ,
• Célia Lemaire 8 &
• André Côté 1 , 2 , 3 , 4 , 5  

BMC Health Services Research volume  22 , Article number:  558 ( 2022 ) Cite this article

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Confusion exists over the definition of the care pathway concept and existing conceptual frameworks contain various inadequacies which have led to implementation difficulties. In the current global context of rapidly changing health care systems, there is great need for a standardized definition and integrative framework that can guide implementation. This study aims to propose an accurate and up-to-date definition of care pathway and an integrative conceptual framework.

An innovative hybrid method combining systematic review, concept analysis and bibliometric analysis was undertaken to summarize qualitative, quantitative, and mixed-method studies. Databases searched were PubMed, Embase and ABI/Inform. Methodological quality of included studies was then assessed.

Forty-four studies met the inclusion criteria. Using concept analysis, we developed a fine-grained understanding, an integrative conceptual framework, and an up-to-date definition of patient-centered care pathway by proposing 28 subcategories grouped into seven attributes. This conceptual framework considers both operational and social realities and supports the improvement and sustainable transformation of clinical, administrative, and organizational practices for the benefit of patients and caregivers, while considering professional experience, organizational constraints, and social dynamics. The proposed attributes of a fluid and effective pathway are (i) the centricity of patients and caregivers, (ii) the positioning of professional actors involved in the care pathway, (iii) the operation management through the care delivery process, (iv) the particularities of coordination structures, (v) the structural context of the system and organizations, (vi) the role of the information system and data management and (vii) the advent of the learning system. Antecedents are presented as key success factors of pathway implementation. By using the consequences and empirical referents, such as outcomes and evidence of care pathway interventions, we went beyond the single theoretical aim, proposing the application of the conceptual framework to healthcare management.

Conclusions

This study has developed an up-to-date definition of patient-centered care pathway and an integrative conceptual framework. Our framework encompasses 28 subcategories grouped into seven attributes that should be considered in complex care pathway intervention. The formulation of these attributes, antecedents as success factors and consequences as potential outcomes, allows the operationalization of this model for any pathway in any context.

Peer Review reports

While having a performant healthcare system is a crucial issue for every country, the health sector operates in silos that need to be challenged. Indeed, many authors have pointed to fragmented care processes as a cause of breakdowns in the continuity of healthcare services [ 1 ], unnecessary waiting times [ 2 , 3 ], flaws in the flow of information between the different episodes [ 4 ] and the realization of exams that may be superfluous [ 5 ]. This fragmentation results in a sub-optimal use of material and financial resources and unsatisfactory team management [ 4 ]. Based on this observation, several repeated calls to improve the quality and performance of healthcare services have been made since 2001 by national and international institutions such as the Institute of Medicine of America (IOM) in 2001 [ 6 ] and 2013 [ 7 ], the National Academies of Sciences, Engineering, Medicine in 2018 [ 8 ] and the World Health Organization (WHO) in 2016 [ 9 ] and 2020 [ 10 ]. These calls have progressively shifted from an injunction to improve quality based on criteria to provide safe, effective, efficient, timely, equitable and patient-centered care [ 6 ], to the development of models for the organization of health care and services that meet the current challenges of effectiveness and efficiency in healthcare systems. The WHO urges member countries to base their quality improvement policies on the entire continuum of care, taking into account at least the criteria of effectiveness, safety, equity, efficiency, integrated care and timeliness [ 11 ]. These calls also emphasize the need to improve care pathways by focusing on outcomes that matter to the patient from a clinical, quality of life and health system experience perspective [ 12 , 13 , 14 , 15 ], rather than on the needs of the production units. This change of perspective leads to the study of the redesign of performance evaluation models by focusing on the needs and expectations of the patient [ 16 , 17 ]. The problem is that there is confusion about the definition and characterization of a care and health service pathway. Indeed, Bergin et al. [ 2 ] identified 37 different definitions of the term care pathway based on a review of the literature. Definitions and characteristics vary across countries and include multiple phases ranging from prevention or screening to cure or palliative care. This confusion has led to wide variability in the outcomes of these interventions, resulting in underutilization of care pathway improvement programs [ 2 ]. Furthermore, such confusion leads to great variability in the analysis and modeling of care pathways. For example, in their scoping review, Khan et al. [ 18 ] showed the great variability that exists among studies of oncology care pathways in both the phases of care represented, and their characteristics. The lack of a common definition and clearly defined criteria leads to a lack of standardization, resulting in an inability to conduct reliable comparative studies of care pathway programs internationally [ 19 ].

The Oxford Concise Medical Dictionary 10th ed. [ 20 ] and the Oxford Dictionary of Nursing 8th ed. [ 21 ] define, in a concise way, care pathway as “a multidisciplinary plan for delivering health and social care to patients with a specific condition or set of symptoms. Such plans are often used for the management of common conditions and are intended to improve patient care by reducing unnecessary deviation from best practice”. The concept of a care pathway is one originally used in the field of Health Operations Management, whose definition was proposed by Vissers and Beech [ 22 ]. However, these definitions seem to be too imprecise and address neither the aim nor the social reality of implementing such pathways. The European Pathway Association (EPA) adopts the more precise definition from the 2007 thesis of Vanhaecht [ 23 ]. However this has not yet led to an international consensus, as confusion over the concepts remains high. Moreover, this definition does not clearly define the antecedents or factors favoring the success of such interventions, the means by which to implement them or the best practices through which to support them; nor does it sufficiently take into account the importance of the patient-centered care and patient-centered services approach. Similarly, the proposed implementation models largely neglected the social reality and the social dynamic of organizations [ 24 ], resulting in major implementation difficulties, as care pathways still being considered as complex interventions [ 25 , 26 ].

However, care pathway programs have recently demonstrated encouraging results in terms of reduced variation in care, improved accessibility, quality, sustainability, and cost effectiveness of care [ 2 ]. The definition we aim to develop through this research is significant and timely, in that it has the potential to guide the ongoing development, implementation, monitoring and evaluation of care pathway programs within the rapidly changing service and system contexts that we are experiencing. For example, the following initial barriers to the systemic and holistic implementation of care pathways have recently been removed. Firstly, limited access to valid and reliable data from multiple organizations [ 27 ] has been offset by a massive investment in Electronic Medical Records [ 28 ]. Secondly, the main difficulties in highlighting the complexity of the referral trajectory [ 29 ], frequently resulting from the clinicians’ perspective, have been overcome by proposing new approaches such as data mining or qualitative methods, focusing on the real care trajectory and the qualitative part of the patients’ experience [ 16 , 17 , 30 ]. Therefore, the evolution of knowledge and information technology and the investment of health systems in data-sharing infrastructure, as well as a definition of the levers of patient engagement and the advent of patient-centered-care and patient-centered services, make it possible to define a powerful model for improving them by placing the patient’s needs and expectations at the center of the care pathway. It is therefore the right time to define a recognized definition and an integrative conceptual framework that meets the demand for sharing knowledge internationally regarding the development, implementation, and evaluation of care pathways.

The concept of patient-centered care is defined as “care provision that is consistent with the values, needs, and desires of patients and is achieved when clinicians involve patients in healthcare discussions and decisions” [ 31 ]. This approach is known to provide benefits by improving health outcomes, patient satisfaction, but also to reducing health costs [ 32 ].

A preliminary search for existing reviews was conducted in Cochrane Database, JBI Database of Systematic Reviews and Implementation Reports and PROSPERO. Care pathways have been the subject of few reviews, but these were limited to a single pathology such as cancer in general [ 33 ], blunt thoracic injury [ 34 ], cardiovascular disease [ 35 ], adolescent idiopathic scoliosis [ 36 ] or for particular pathway phases [ 37 ]. In the end, focusing on a single condition is not entirely consistent with a patient-centered approach to care insofar as patients often have comorbidities. The only review that did not focus on one specific pathology was made in 2006 [ 38 ] and was interested in the concept of clinical pathway. Authors reviewed literature published within 3 years using only one bibliographic database. Therefore, the aim of this article is to propose an accurate and up-to-date definition of care pathway and to develop an integrative conceptual framework for the patient-centered care pathway concept in a holistic operational approach of the concept.

Combining systematic review, concept analysis and bibliometric analysis

To achieve a fine-grained understanding of the concept, we have chosen a hybrid method combining the systematic review, the concept analysis and the bibliometric analysis methodologies. We followed the latest PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement for conducting and reporting a systematic review [ 39 ]. However, the systematic review methodology presents some limitations on the qualitative analysis of literature, hence derives our interest to use Concept analysis. Concept analysis [ 40 ] aims specifically to clarify a specific concept including a semantic field linked to a specific theoretical framework. This approach is based on eight steps allowing to: (1) select the concept, (2) determine the aims or purposes of the analysis, (3) identify all uses of the concept, (4) determine the defining attributes, (5) identify a model case, (6) identify additional cases, (7) identify antecedents and consequences and (8) define empirical referents. However, this method does not provide a systematic and rigorous procedure for identifying and selecting relevant literature. Therefore, we decided to combine the strengths of both methods to overcome the limitations of each. In order to make our analysis more robust and to base our inferences, specifically in the comparative analysis of the related concepts, we performed a bibliometric analysis allowing us to link the attributes of each of the concepts to make a comparison.

Information sources and search strategy

We developed a search strategy, in collaboration with a Health Sciences Librarian who specializes in systematic literature review in healthcare, to identify relevant peer-reviewed studies. An initial limited search of MEDLINE and CINAHL was conducted, followed by analysis of the text words containing title and abstract and index terms used to describe the article. This informed the development of a search strategy that was tailored toward each information source. The search strategy was applied to the following databases: PubMed, Embase and ABI/Inform. The complete search strategy is provided in Additional file  1 .

Eligibility criteria

This review considers studies that focus on quantitative and/or qualitative data, with no limitation in terms of methodology. Our search focused on peer-reviewed scientific articles. Therefore, books, doctoral or master’s theses were excluded due to time and resource limitations. In order to guide the selection, we chose the Population, Context, Concept (PCC) mnemonic criteria [ 41 ]. The population considers all types of patients managed by healthcare delivery systems. The context studied is composed of healthcare providers in any geographic area, including all providers of primary, secondary, tertiary, and quaternary care. For the concept, this review focuses on theoretical and empirical studies that contribute to the definition and conceptualization of the different related concepts of care processes at the organizational or system level, such as care pathway, clinical pathway, patient journey and care processes. Quantitative, qualitative and mixed method studies involving a single episode of care limited in time (a one-time treatment) or space (a single hospital service/department) were excluded to the extent that care pathway involves multiple points of interaction over time [ 13 , 42 ] and multiple organizational structures or intra-organizational entities along the care continuum [ 43 ]. In addition, studies with no theoretical or conceptual input were excluded. Finally, there was no language or geographic restrictions applied to the search, and the study period was limited from 1995 to 2020.

These studies were imported into the Covidence® software (version 2020). The team developed screening questions and forms for levels 1 (abstract) and 2 (full text) screening based on the inclusion and exclusion criteria. Two reviewers independently screened the titles and abstracts. In case of disagreement, two senior reviewers decided after analysis and discussion. Review author pairs then screened the full-text articles against inclusion and exclusion criteria. In case of disagreement, the same process as for the title and abstract selection was implemented. Reasons for excluding studies were recorded.

Assessment of methodological quality

Because of the heterogeneity of the methods used in the selected articles, we decided to use a separate appraisal tool for each study type. The following appraisal tools were selected for their clarity, relevance, and because their items covered the most common assessment criteria comparing to other tools:

For qualitative studies: the JBI Qualitative Assessment Research Instrument (QARI) [ 41 ]

For surveys: the Center for Evidence Based Management (CEBMa) Appraisal Questions for a Survey [ 44 ]

For descriptive cross-sectional studies: the Institute for Public Health Sciences 11 questions to help you make sense of descriptive/cross-sectional studies [ 45 ]

For mixed-method: the scoring system for appraising mixed methods research [ 46 ]

No articles were excluded from this systematic review due to the weaknesses of their methodological quality, so as not to exclude valuable information [ 47 ].

Data extraction and analysis

Descriptive numerical summary analysis followed the systematic review guidelines, and the following items were systematically extracted: Reference, Title, First Author country, Case country, Year of publication, Type of publication, Target patient population, Phases of the pathway included, People involved in the modeling process, Study parameters and level of analysis.

Qualitative data were extracted using MaxQDA® software (version 2020) by two independent analysts. The data extraction followed the concept analysis guideline [ 40 ] and the following items were systematically extracted: Variant concept studied, Concept uses, Concept definition, Concept attributes, Antecedents, Consequences and empirical referents. In order to develop a detailed analysis and arrive at a robust theoretical framework, we relied on general inductive analysis [ 48 ], consisting of coding, categorization, linking, integration and modeling. Each step has been validated by at least two senior authors.

A bibliometric analysis was performed with the complete texts of the 44 selected studies using Vosviewer® software (version 2020).

The systematic review was reported following the latest PRISMA statement for conducting and reporting a systematic review [ 39 ] and mobilized the PRISMA 2020 checklist (see Additional file  2 ).

The interrogation of the three databases resulted in 15,281 articles. Figure  1 details the selection process following the PRISMA 2020 statement [ 39 ]. After deleting the duplicates, 15,072 records were reviewed but only 44 publications ultimately met the inclusion and exclusion criteria.

PRISMA 2020 flow diagram of the systematic review process

Description and methodological quality appraisal of studies

A summary table containing a brief description of selected studies and their evaluation results for methodological quality is presented in Table  1 . Quality appraisal of selected studies is presented in Additional file  3 .

Published articles, describing care pathways as multiple points, in time and space, of patient interaction appeared in the early 2000s. However, most of this work has been published since 2010, with a progressive and growing interest, whatever the theoretical position, to reach 22 articles in the last 3 years (see Fig.  2 ).

Frequency of selected publications over time

The countries of the first authors interested in this concept are predominantly anglophone such as the United Kingdom (k = 9), Australia (k = 5), the United States (k = 4), and Canada (k = 3). Researchers from other countries are less represented.

Three types of publications were found; 34 were original research studies, eight were literature reviews and two were perspective studies. In the original research studies, 23 used a qualitative approach to study either the implementation of a care pathway program or patient experience of a care pathway, four used a descriptive cross-sectional approach, four used a mix-method approach and three used a survey.

Since the definition of the concept is still unclear and terminology is important, the studies meeting the selection criteria reported several terminologies. The most frequently used terms in the selected studies were the patient journey (k = 14) and the care pathway (k = 13) with their some country-specific modifications namely integrated care pathway mainly in the United Kingdom [ 73 , 74 ], optimal care pathway in Australia [ 2 ] and standardized care pathway in Sweden [ 15 ]. The other terms used were clinical pathway (k = 8), patient-centered care (k = 4), care process (k = 3), disease pathway management (k = 1) and value-based integrated care (k = 1).

Studies focused mainly on the care of chronic conditions (k = 24), followed by acute diseases (k = 11). Of those with a chronic care focus, cancer was by far the most studied disease (k = 10), followed by stroke, hearing impairment and mental disease. Acute care studies covered, articular pathologies of the hip and knee, and pregnancy.

Concerning the level of the study, most addressed the systemic (k = 31) rather than the organizational (k = 13) level. Most authors, in their approach to the concept, largely focused on the treatment phase (k = 39), but some included, more or less, pretreatment and subsequent phases. Only seven articles took a global approach starting from the prevention phase and screening to survivorship or palliative care phase.

Concept analysis results

The conceptual analysis followed an automatic data extraction method in the proposed main categories and then, after several iterations, resulted in a coding of subcategories grouped into main themes. The detailed results of the coding are presented in Additional file  4 .

Concept uses

Uses of the concepts of care pathway have evolved in the literature over time with a strong tendency to focus on the care pathway at the systemic level. Main objectives have been improving quality and safety (k = 26), improving efficiency in the delivery of care (k = 24), optimizing the delivery process through an operation management point of view (k = 22) and integrating best practices through guidelines and evidence-based medicine (k = 17). These objectives were widely shared and present throughout the period. However, interest emerged in 2009 and quickly grew, in improving the patient experience through the analysis of the patient journey (k = 17). To a lesser extent, the goals of developing patient-centered care (k = 13), improving patient outcomes (k = 13), improving coordination of service delivery (k = 13), and standardizing care delivery (k = 12) were also present. Beyond standardization, reduced variation in care practices (k = 9) was not well addressed, nor was continuous performance assessment (k = 8). The aim of meeting the patient’s needs (k = 6) has been addressed more frequently in recent years, since its first appearance in 2011 [ 71 ], and is considered of crucial importance by some authors. Other concept uses were proposed, such as to improve interprofessional collaboration (k = 5), support changes (k = 5), support clinical decision making (k = 4), improve communication (k = 3), consider needs of healthcare workers, improve referral system, define shared purposes and meaningful objectives (k = 2), monitor staff compliance, support the knowledge management, improve patient and family member access to information, adopt a system approach and understanding power dynamics and relational factors (k = 1). As described previously, these concept uses came mainly from the chronic disease care context, although acute care was also represented.

Defining attributes

Definitional attributes are features commonly encountered in definitions of the concept or frequently used to describe it [ 40 ]. Twenty-eight attributes were inductively extracted and categorized into seven main themes, ordered by level of empirical importance: (1) The centricity of patients and caregivers; (2) the positioning of professional actors involved in the care pathway; (3) the operation management through the care delivery process; (4) the particularities of coordination structures; (5) the structural context of the system and organizations; (6) the special role of the information system and data management; and (7) the advent of the learning system (k = 3).

Attribute theme 1: The centricity of patients and caregivers

Firstly, there has been a growing interest in the patient experience (k = 15), mainly through the concept of the patient journey [ 5 , 13 , 14 , 15 , 24 , 30 , 42 , 51 , 52 , 58 ], which has progressively emerged as the third pillar of quality in healthcare with clinical effectiveness and patient quality and safety [ 30 ]. It is formed by all the interactions at the meeting point, or point of contact, between health services and patient [ 14 , 30 , 42 , 51 ]. However, taking the patient experience into account is complex insofar as it requires a detailed understanding of what influences it. Therefore, some authors have defined the dimensions that can influence the patient experience as the temporal dimension, meaning that accessibility and short waiting times are valued [ 13 , 15 , 30 , 42 , 51 ], the spatial dimension [ 30 ], and the geographical position of the services [ 42 ], the emotional dimension [ 13 , 30 , 42 ] and the social and cognitive dimensions [ 13 , 42 ]. All these dimensions can be the source of both positive outcomes [ 13 , 30 ] and negative outcomes [ 15 ] or for socio-political authors, a feeling of considerable disempowerment [ 53 ]. Although authors are increasingly interested in it, the patient experience is still sometimes overlooked [ 14 ].

Patient information and education (k = 15) were addressed in numerous studies. Patient information contributes to the quality of the patient experience [ 3 , 15 , 36 , 42 , 53 , 64 , 71 , 75 ]. Beyond the simple satisfaction, the provision of information, at an appropriate health literacy level, increases patient awareness [ 36 , 51 ] and thus increases patient education. This results in a better detection of the symptoms at an early stage by the patient [ 3 , 36 ], the development of the “expert patient” [ 51 , 57 , 58 , 71 ], which aids adherence to treatment, supports shared decision-making [ 57 ] and improves self-management [ 51 , 58 ]. However, many empirical studies showed there to be a lack of patient information throughout patient journeys [ 5 , 14 , 15 , 42 , 51 , 53 , 64 ].

Patient engagement (k = 15) was an important attribute of this theme in the more recent literature. The management by the patient of his or her care treatment plan has become increasingly important [ 24 , 50 , 51 , 53 , 67 ]. This translates into shared decision-making on care and treatment [ 3 , 14 , 24 , 35 , 51 , 53 , 55 , 54 , 55 , 58 , 64 , 65 ]. According to Devi et al. [ 51 ], this process can only be viable if supported by good information about treatment possibilities and possible outcomes. However, socio-political authors see this as a major issue of patient empowerment, which is “seen as a solution to many of the most pressing problems facing modern healthcare” [ 53 ].

Proposed only since 2014, and strongly present in the last 3 years, relationship as the basic need (k = 9) is also a subject of interest. Part of the patient experience, the relational quality reflects how patients perceive their interactions [ 13 , 42 ]. Some empirical studies have shown that a poor relationship can negatively affect other processes and tasks [ 3 , 5 ]. Therefore, quality of the relationship seems a fundamental prerequisite [ 14 , 64 ]. For this reason, some authors have placed the notion of trust as essential to the quality of interactions and to the patient’s follow-up through the care pathway [ 3 , 12 , 58 ].

Patient and Public Involvement (k = 9) is part of these new topics. Its importance in the design and improvement of the care pathway is supported by some international organizations [ 9 ]. The objective is to improve the quality of care provided by assessing patients’ perceptions [ 12 , 13 ]. In this way, the design of care delivery can be based on the real needs and expectations of patients [ 12 , 13 , 51 , 56 , 62 ]. However, some models have been criticized as tokenistic rather than being viable solution for balancing power between patients and health care providers [ 53 ].

Although the stated goal of care pathways incorporates an approach aimed at standardizing care practices, several authors have raised the need for individualized care (k = 8). Joosten et al. [ 74 ] saw a potential conflict between standardization and the demand for a personalized approach to healthcare. However, several authors have subsequently agreed that there is still room for individualization of care beyond the standardization [ 55 ], in particular through the definition of personalized treatment goals [ 51 ], or even maintaining flexibility in the interaction to better adapt to the patient’s specific needs [ 64 , 65 ].

Developed only since 2016, the importance of psychosocial support (k = 8) has increased rapidly. Although the need has been clearly identified and documented [ 5 , 15 , 42 , 58 ] and many international guidelines have integrated it, it seems that its translation within the care pathway is still complex [ 62 ] and no obvious answer was provided.

The inclusion of family and caregiver (k = 8) is also a new topic of the last 5 years which highlights the potential of family or caregivers involvement in decision-making [ 50 , 51 , 57 , 65 ]; notably by supporting both the integration of information and personal decision-making [ 14 , 15 ].

Attribute theme 2: The positioning of professional actors involved in the care pathway

Firstly, most authors consider the care pathway as a tool to develop patient-centered care (k = 18). The patient-centered care approach has a disease-specific orientation [ 25 ] and considers the patient as a real partner [ 51 , 25 ]. In doing so, this approach recognizes an individual’s specific health needs and preferences as the driving force in all healthcare decisions [ 13 , 51 , 65 , 67 ]. Thus, professional actors emphasize their accessibility and their attitudes and behaviors towards patients [ 13 ]. In addition, this approach considers the importance of integrating family and caregivers and is recognized as a necessary attribute of healthcare quality [ 65 ]. Finally, its implementation seems to improve patient satisfaction by moving toward an individualized therapy approach and personalized treatment goals [ 51 ].

Not surprisingly, multidisciplinary team-working (k = 17), and attribute which is consistent with previous definitions, is supported by several authors. The enrollment of all professional categories involved directly or indirectly in the care pathway at all steps is valued [ 2 , 50 , 75 ]. The multidisciplinary teamwork allows tackling the complexity of patient care across the pathway and developing a shared understanding supported by knowledge sharing among professionals [ 53 , 72 ]. In addition, it allows outlining the optimal sequence and timing of interventions [ 38 , 59 ] and to focus only on patient needs and engagement rather than on problems of a particular profession [ 56 ]. From an operational view, multidisciplinary care teams make it possible to share formal screening between disciplines [ 62 ]. Recently, multidisciplinary engagement was identified as a mandatory prerequisite for successful care pathway programs [ 24 , 50 ].

Staff skills (k = 10) could be considered equally important for care pathways. However, they were not addressed in this literature before 2014. Authors gave little attention to technical skills, except to point out possible deficiencies, particularly in diagnosis [ 3 , 13 ], but also in training [ 3 ]. Rather, authors focused almost exclusively on interpersonal skills [ 3 , 12 , 13 , 15 , 51 , 64 ], which were considered critical, both in the relations between professionals [ 12 , 15 , 51 , 56 , 64 ] as well as those with patients and their caregivers [ 15 , 51 , 64 ]. Interpersonal skills could be seen as facilitators or barriers to the patient experience [ 64 ]. Some authors have recently suggested that peer cooperation was critical [ 5 , 50 , 56 ] and that creating a culture of mutual respect among both medical and administrative colleagues can ultimately improve the fluidity of care [ 3 , 5 ].

Few authors have highlighted that the implementation of a care pathway leads professionals to examine their roles and responsibilities (k = 6). The need to define each step in the care process requires professionals to describe precisely the tasks and roles of professional actors [ 25 ]. In doing so, it creates a rare opportunity to step back from daily tasks and reassess competences, roles and responsibilities [ 12 , 51 , 73 ].

Finally, very recently, authors have been interested in the experience of staff (k = 2) in care pathway programs. These authors have demonstrated the link between staff experiences and their individual performance [ 24 , 53 ]. They therefore support the idea that staff well-being is directly related to engagement and performance and, thus, a negative staff experience can influence patient, clinician, and organizational outcomes.

Attribute theme 3: The operation management through the care delivery process

This analysis has shown, unsurprisingly, that the process approach to care delivery (k = 23) was the core of the care pathway approach across the literature to date. From an engineering perspective, as define by the International Organization for Standardization, a process is “a set of interrelated or interacting activities that transforms inputs into outputs” (ISO 9000:2000 clause 3.4.1). Through this approach, the care process can be defined as an arrangement of tasks or actions sequenced in time resulting in a time matrix [ 24 , 30 , 38 , 52 , 60 , 68 , 25 , 73 ]. What distinguishes the different process approaches to care delivery are the tasks and actions included with them. Some authors tend to focus on operational planning by treating tasks, actions and their timing through business processes [ 43 , 49 , 54 , 60 , 69 ], while other authors consider both the context of action through the physical and organizational environment [ 24 , 30 ] and social dynamic through the experience of actors [ 24 , 52 , 53 ]. Through this approach to care processes, some authors focus on patients and caregivers [ 52 ] and other authors focus on human actors, both patients and caregivers and the professional actors involved in the care pathway [ 24 ]. In 2018, Ponsignon et al. [ 13 ] proposed to differentiate the direct, indirect and independent interactions (those disconnected from the delivery system), in care processes. Direct interactions constitute the points of contact between patients and the system, and so are responsible, along with indirect interactions, for the patient version of the pathway that some authors call the patient journey [ 5 , 13 , 30 , 51 , 53 ]. More recently, the complexity of the care process has led some authors to consider that the care pathway should involve pathway rules which control the process [ 70 ]. Thus, decision-making becomes a central element in the smooth running of the care pathway [ 60 ]. In addition, many authors consider that healthcare decisions and care pathways are intertwined so that it becomes imperative to co-design both care pathways and the decision-making activities [ 60 ].

The issue of process management for the delivery of care naturally raises the question of process modeling methods (k = 18). In the empirical articles, the use of the Business Process Modeling Notation (BPMN) developed by the Object Management Group seems to be progressively imposed, sometimes improved by decision modeling [ 4 , 43 , 54 , 60 , 68 , 69 ]. The use of process mapping or flowcharts with sometimes less formal rules seems to be favored for global approaches to processes, especially for the patient journey, although some authors such as Combi et al. [ 60 ], have demonstrated that BPMN modeling was quite compatible with the systemic approach.

For healthcare service designers, the methods for building care pathways are important considerations. Several methods exist, but all involve the discovery of a different path, thus change is inevitable and change management a necessity. The initial method came mainly from the expertise of professionals through interviews, focus groups or Delphi methods [ 49 , 59 ]. The advantage of collaboration with staff and experts is that more information can be gathered about certain decisions and possible variances from the pathway [ 49 ]. However, this method did not consider the real trajectory or the ideal pathway but rather the one integrating the constraints of the professionals. Since these early efforts, data driven approaches has developed considerably [ 43 , 49 ]. Their advantage is that they inform pathway development from data derived factually and objectively from actual occurrences of the pathway [ 49 ]. Moreover, data on the perspectives of patients through experience mapping, interviews, focus groups or observations [ 5 , 13 , 30 ], and patient shadowing [ 53 ] can be integrated to better reflect the real trajectory and to define the ideal pathway according to the needs and expectations of patients and caregivers. However, this approach does not allow for the integration of context and organizational constraints. Finally, few authors adopt an approach that consists of comparing the experience of professionals and patients, making it possible to define the lived experience, the patient’s journey, and its confrontation with operational realities and constraints through the experience of professionals [ 1 , 3 , 4 , 15 , 65 , 71 ].

Regarding the process of care delivery, the management of operations aims to integrate the organization of the delivery process with its ongoing improvement (k = 11) by focusing as much on analyzing the variations as on eliminating the wastes [ 74 ]. Process improvement tools serve as much to redesign the processes as define a workflow management system to monitor the care pathway [ 4 ]. The information generated [ 60 , 61 , 63 ] can be used for process re-engineering, objective reassessment or supporting non-clinical decision-making [ 60 ], such as the identification of bottlenecks [ 61 , 67 ] or highlighting interfacing problems between organizations [ 61 ]. The output generated by the analysis of the process-related data allows defining standardized expedited diagnostic processes [ 4 , 60 ]. Finally, the data obtained allows the use of simulation and optimization models. On this subject, Aspland et al.’s literature review [ 49 ] provides an exhaustive review of available methods.

Attribute theme 4: The particularities of coordination structures

In line with most of the definitions, the integration of the clinical practice guidelines, based on evidenced-based medicine, into the care pathway (k = 24) has been accepted since the beginning of such programs. The clinical decisions directly affect the flow of the care delivery process and thus the process performance and the quality of outcomes [ 60 ]. Therefore, the adherence to clinical practice guidelines must support decision-making [ 70 , 73 ] and aid diagnosis and treatment in order to improve patient outcomes [ 50 , 51 , 58 ]. In 2010, Vanhaecht et al. [ 25 ] expressed concern about a lack of evidence-based key interventions within care pathways. The care pathway can be an effective method to integrate and guarantee the appropriate use of evidence-based interventions and clinical practice guidelines [ 55 ] and may help to overcome two limitations of clinical practice guideline use, which are emerging as key issues [ 60 , 66 ]. Firstly, that they should not be followed blindly as they represent only explicit medical knowledge [ 67 ], but rather require integration of the contextual knowledge of healthcare professionals for appropriate use [ 72 ]. Secondly, it has been shown that physicians can be unaware of updates and changes to clinical guidelines [ 3 ], and so, integrating them into care pathway maps may improve guideline use and adherence. Finally, collectively integrating and discussing clinical practice guidelines appears to improve interprofessional collaboration and clarify roles [ 36 ], but also could benefit the involvement of patients in the co-design of the care pathway [ 35 ].

Some authors consider information continuity (k = 13) as a key factor. Not only because sharing information must support decision-making [ 60 , 75 ] and facilitate communication [ 2 , 12 , 38 ], but more broadly because the disruption of the information flow can lead to coordination problems and easily avoidable costs linked to the repetition of examinations [ 5 , 56 , 59 ]. Therefore, the continuity of information must be supported to ensure sustainable health improvements [ 51 , 70 ]. Some authors insist on the importance of defining an information medium throughout the pathway which is as accessible to care professionals as it is to patients and caregivers [ 65 ].

Recently, some authors have dealt with the subject of leadership of the care pathway (k = 9). The importance of defining a leader for each step of the care pathway was noted [ 25 ]. The lack of coordination without a responsible actor has been shown, especially when the care pathway includes actors in several contexts such as primary care [ 3 ]. Thus, new roles have been defined, such as case managers, joint program or nurse coordinators [ 4 , 15 , 42 , 65 ], roles that enhance coordination among providers through the improvement of the continuity and quality of the information as well as communication [ 15 ].

More recently, the integration of services (k = 9) has been addressed. Because the care pathway approach can involve multiple partnerships between organizations and primary care, it is essential to integrate all stakeholders. The integration needs to be both organizational, at the macro and meso-level through shared purpose and priorities [ 4 , 57 , 25 ] and shared governance mechanisms [ 4 , 12 , 14 , 59 ], and functional at the micro level through communication mechanisms and tools [ 4 , 12 , 14 ]. The unifying element is discussed between the shared interest for the patient [ 56 , 57 ] or the outcomes [ 12 ] to align strategic goals. For Louis et al. [ 56 ], achieving shared purpose is part of the structural context.

Finally, the care pathway is seen as a means of health knowledge management (k = 7) that optimizes quality, efficiency, and organization [ 68 , 70 , 72 ]. But this topic, although strongly addressed between 2011 and 2012, did not seem to be unanimously agreed upon because it was not very well addressed afterwards. However, particular attention can be paid to the elicitation and integration of the contextual knowledge of the various actors involved throughout the care pathway into daily healthcare routine [ 3 , 70 , 72 ].

Attribute theme 5: The structural context of the system and organizations

Firstly, the local physical context (k = 10), topical in the recent literature, includes both the number of units and their positions [ 12 , 67 ], but also the variety of services offered [ 13 ], and can be either an asset in terms of choice and accessibility or a constraint becoming a source of delay [ 14 ]. These barriers are important as the pathway crosses several formal healthcare organizations or informal care settings [ 24 ]. Therefore, the challenge of service integration has become essential [ 51 ].

Secondly, the availability of resources (k = 10) (human, material and financial) has a direct impact on the care pathway and the ability to meet the needs of the population [ 2 , 62 , 25 ]. A lack of adequate resources is an obvious obstacle to care pathways [ 50 ]. A lack of material and human resources, such as the availability of time at each service point [ 52 , 53 ], or the lack of an electronic medical record [ 5 ], meant the unnecessary repetition of history taking, examinations and full investigations. From a financial point of view, the financial and personal resources that people have, are also key to determinants of the care pathways followed by patients [ 51 ].

Thirdly, the social context (k = 7) is less addressed in the current literature but has shown rapid growth in recent years. Social structure includes material and social resources including roles, rules, norms, and values [ 3 , 24 , 53 , 68 ]. Some authors consider the social context as regularities of perception, behavior, belief and value that are expressed as customs, habits, patterns of behavior and other cultural artifacts [ 68 ]. Other authors consider that social structures shape people’s actions and that through people’s interactions they can then reproduce or change these social structures [ 53 ]. While others consider, for their part, that social and physical contexts can be at the origin of boundaries that mitigate against collaboration, adding to the complexity of shared clinical practices in this field [ 3 , 24 ].

Attribute theme 6: The special role of the information system and data management

Data management (k = 14) plays an increasingly important role in the analysis and improvement of care pathways. The implementation of a care flow management system aligned to clinical workflows [ 67 , 69 ], allows real-world data to be used [ 51 ], and visualized through performance dashboards to generate timely corrective action [ 4 ]. It also enables the analysis and monitoring of the variance in time and space within care pathways [ 43 ]. It is considered responsible for the rise of accountability [ 12 , 75 ].

The Electronic Health Record system is a support tool (k = 13) in several aspects. Numerous authors consider that it supports the patient-centered approach [ 51 , 67 ]. In particular, it has the capacity to support communication between health professionals, and between them and the patient [ 5 , 12 , 65 , 67 , 73 , 75 ], but also to support healthcare knowledge learning [ 67 , 73 ], and integrate clinical decision support into IT applications and clinical workflows [ 70 ]. This support throughout the care pathway can improve the quality of care and health outcomes by reducing medication errors and unnecessary investigations [ 5 ]. As stated by Fung-Kee-Fung et al. [ 4 ], the information system provides the fundamental connectivity across silos and professional groups to support the creation of care pathways and sustainable change at the system level.

The issue of digitalization (k = 5) has been treated very recently. It raises the issue of system integration throughout the care pathway. Despite the technological advances and the support of international organizations such as the guidelines on evidence-based digital health interventions for health system strengthening released by the WHO [ 76 ], there are still inefficiencies associated with trying to integrate EHRs across organizations [ 56 ]. These are frequently due to the use of different technological solutions by different stakeholders [ 30 ]. The challenge is therefore to propose a model for integrating information systems throughout the care pathway that are accessible to all stakeholders including patients themselves [ 4 , 50 , 51 , 65 ].

Attribute theme 7: The advent of the learning system

Although it was not frequently addressed, some authors have developed, very recently, the importance of setting up a learning system (k = 3) to support the care pathway. Resulting from the work of Quinn [ 77 ] and Senge [ 78 ], it consists of the development of a system to learn from itself and its past experience and improve the effectiveness, efficiency, safety, and patient and family/caregiver experiences [ 65 ] through a feedback loop [ 24 ]. Data on outcomes can be used as feedback to identify improvement opportunities at various stages of the process or at specific interfaces between stakeholders. The learning system promotes “individual competence, systems thinking, cohesive vision, team learning, and integrating different perspectives” [ 4 ].

Related concepts

The related concepts are confusingly close or even integrated with the main concept studied [ 40 ]. Given the complexity of the use of concepts, we have relied, in addition to definitions found on an analysis of a bibliometric network by integrating all 44 articles, excluding abstracts and bibliographies, into the Vosviewer® software (version 2020). The results help us to refine our understanding of the concepts which define the links between the different keywords. The care pathway bibliometric links are provided as a comparator (see Fig.  3 ).

Care pathway bibliometric links

Clinical pathway (Fig.  4 ) was initially defined by De Bleser et al. [ 38 ]. It is a multidisciplinary intervention that aims to integrate the guidelines into daily routine and manage medical activities in order to improve the quality of service and optimize the use of resources [ 70 ]. It integrates a process of care approach [ 72 ] and aims at standardize care on a procedure or an episode of care [ 38 , 49 , 68 ], integrating decision-making supported by knowledge. What differentiates it from the care pathway is that it is restrained in time and is anchored in an organization [ 25 ], or even a service, and does not deal with the patient experience in any way. Clinical pathways are thus integrated in care pathways at the local level and focus on a single phase of care.

Clinical pathway bibliometric links

Patient journey (Fig.  5 ) consisted of sequential steps in the clinical process of the patient through their experience. It can be defined as “the spatiotemporal distribution of patients’ interactions with multiple care settings over time” [ 24 ]. By analyzing and mapping the patient experience from their perspective [ 5 , 14 , 57 , 58 , 71 ], the objective is to improve the quality of the service provided [ 14 , 52 ]. In this approach, the patient journey is an integral part, and an essential component, of the care pathway. Although it also integrates the process approach, it is not linked to decision-making or knowledge management and does not consider structural constraints or the perception of the providers.

Patient journey bibliometric links

Finally, the care process (Fig.  6 ) is involved across the care continuum to standardize and streamline end-to-end care using management tools [ 4 ]. It is directly linked to the care pathway, the clinical pathway and the patient journey. However, although it supports coordination through decision-making and knowledge management, it does not consider the patient experience, the social relationships and the social dynamics. So, the care process is an integral part of the care pathway but does not consider all the characteristics of the latter.

Care process bibliometric links

Antecedents of the concept

Antecedents are events occurring or in place before the concept can emerge [ 40 ]. Our analysis has highlighted several prerequisites for care pathway implementation (see Additional file 4 ).

Firstly, several authors have stressed the importance of the availability of managerial skills (k = 10). They recommend the creation of a change management team [ 49 , 55 ] consisting of a multidisciplinary team integrating not only knowledge about care pathways [ 60 , 70 ], but also knowledge about operations research, information systems and industrial engineering [ 49 , 55 ]. In addition, some authors advocate the presence of key change leaders in the group included clinicians, administrators, IT leaders, process experts, data analysts, nurses, and patient and family members [ 4 , 24 ]. The project leaders must be available on a long-term basis [ 50 , 75 ], have the ability to understand system interdependencies [ 24 ] and have the ability to create a safe learning environment in which openness is encouraged and everyone’s opinion is valued [ 3 , 50 ]. This could be achieved by using consensus-driven approaches that could address institutional process barriers, resistance to change, and conflicting targets and priorities [ 4 ].

Secondly, care pathway projects should have a priori the adequate resources (k = 4), but their availability must be verified [ 62 , 75 ]. The presence of an EHR is necessary to have access to reliable data at the pre-analysis phase and during the implementation phase to identify the relationships between the context, the mechanisms and the results obtained [ 2 , 73 ].

Finally, other key success factors emerged from the literature (k = 10). Some authors noted that rules of co-involvement and a bottom-up strategy was needed [ 55 ]. Other authors emphasized that the selection of areas where there were clearly established deficiencies was essential given the cost of such projects, but also that the identification of any subgroups for whom its use may not be appropriate, was also required [ 73 ]. They highlighted the importance of following guidelines to achieve professional adherence [ 2 , 50 , 62 , 72 , 73 ], while maintaining flexibility in the approach to implementing a care pathway improvement program [ 62 ]. They also pointed to the importance of communicating on the progress of the project [ 50 ] and of monitoring the applicability of daily work tasks [ 73 ]. Finally, they consider it essential to embed the pathway into policy and strategy [ 2 , 50 , 72 , 75 ]. While others, for their part, highlighted the importance of defining an iterative feedback loop for individuals and aggregated operational and clinical data [ 4 , 24 ].

Consequences (outcomes) and identification of empirical referents

Consequences are events that are the results of the mobilization of the concept [ 40 ] and empirical referents, for their part, consist of observable phenomena by which defining attributes are recognized [ 40 ] (see Additional file 4 ). In a larger sense, this could be the Key Performance Indicators (KPIs) by which one can recognize the defining attributes and their outcomes.

Although the terms of quality and safety, efficiency and process improvement were the first themes in terms of aims, the most frequently occurring theme in the findings pertained to effects on the patient experience (k = 16). These were measured in different ways, including the impact of waiting times (k = 10), patient satisfaction (k = 7) and the patient quality of life (QALYs) (k = 4). There were also attempts to analyze the patient experience more broadly (k = 5), and to integrate patient needs into the redesign of the care pathway [ 5 , 13 , 56 ].

Efficiency of care (k = 15) was strongly supported by some authors as a desired outcome in care pathways. This outcome was first seen, as an objective, through the costs and cost effectiveness of programs [ 49 , 55 , 61 , 70 ], however, more recently it has been considered a consequence of process improvements, rather than a program objective. It has been clearly defined as the reduction of costs through the reduction of the use of healthcare services [ 57 ]. Moreover, reduction in time spent in care, such as the length of stay or cycle time [ 2 , 55 ], is commonly the consequence of process improvements.

Quality of care (k = 11) was addressed but much less frequently than expected. In the global approach, time to diagnostic is a good empirical referent to analyze the capacity of the first steps of the care pathway [ 4 , 69 ]. Other referents such as reduction of unnecessary investigations and medication errors are also addressed but the number and types of complaints were addressed only by socio-political authors [ 53 ].

Health outcomes (k = 11) were also proposed but only since 2009 [ 73 ]. Clinical outcomes and mortality rates are empirical referents that are unanimously accepted. Recovery time and readmission rates were less frequently considered. Single disease index evaluation was proposed by very few authors [ 49 , 70 ].

Process metrics and patient flow (k = 11) was addressed but only the execution time was unanimously accepted as an empirical referent. Apart from the process variance which is shared, only few authors have developed other KPIs such as the percentage of pathway completion [ 70 ], and evaluation for the reasons of pathway failure [ 70 ].

The variance of practices (k = 9) was not frequently addressed as an empirical referent; however, this is one of the objectives of the care pathway addressed in the literature. The introduction of guidelines [ 2 ] aims to decrease the variation within or between practices (k = 3).

Continuity of care (k = 6) was poorly addressed, even though we might assume that this is one of the primary objectives of the care pathway. This may be due to the difficulty of providing tangible results given the duration of such interventions.

Some authors noted an improvement in documentation and data collection (k = 5), measured by rate of documentation [ 54 ], the ability to better understand resource adequacy (k = 3) and a better comprehension of the links between decision outcomes and process performance (k = 2).

Not defined as an outcome, the Human Resources metrics are proposed by some authors and notably diagnostic quality and referral appropriateness, professional competences and staffing levels. Only Carayon et al. [ 24 ] proposed to integrate the quality of working life as an indicator, based on the principle that well-being at work has a direct impact on individual performance and on the results of the care pathway.

Moreover, not present in the empirical references, the measure of the team relationship and coordination (k = 4) has been proposed by some authors, however, the type of indicator has not been clearly explained.

An integrative definition and conceptual framework of patient-centered care pathways

Given the results of our systematic review and concept analysis and our main objective of defining an integrative framework, we suggest the following definition:

“A patient-centered care pathway is a long-term and complex managerial intervention adopting a systemic approach, for a well-defined group of patients who journey across the entire continuum of care, from prevention and screening to recovery or palliative care. This intervention:

prioritizes the centricity of patients and caregivers by analyzing the patient experience through their needs and expectations, taking into account the need for information, education, engagement and involvement and integrates the patient relationships as a fundamental need.

supports the roles of professional actors involved in the care pathway by developing adherence to the patient-centered care approach; working on interdisciplinarity through the development of skills, both technical and above all relational; the clarification of roles and responsibilities; and by taking into account the experience of professionals both in understanding the organizational constraints and their well-being at work.

integrates a process of care approach through the modeling and improvement of the care pathway by continuously integrating the latest knowledge and information to support clinical decision-making and by defining feedback loops to continuously improve clinical and non-clinical process supported by operation management contained within process improvement methodology approaches;

embeds coordination structures through: the implementation of best practices and the translation of guidelines into daily practice; the support of informational continuity through the integration of services at the systemic level; the implementation of knowledge management along the care continuum; and the identification of leaders at each step of the care pathway;

adapts to the contexts of both the physical and social structures by integrating the human, material, economic and financial resource constraints, as well as the social dynamics of power and trust relationships;

is supported by information systems and data management, enabled by digitalization, which ensure the flow of information within the right context at the right time and place, and allows the continuous integration of the latest knowledge into the care flow and the management of accessible data in real time to monitor and evaluate variances in practices and outcomes;

promotes the development of a learning health system to support the care pathway.

The aim and shared goal of a care pathway is to meet the needs and expectations of patients through continuous improvement of patient experience, patient outcomes, quality and safety while taking into account operational and social realities of the system.”

We know that this definition is important but feel that there is a great need for clarification of this concept and how these interventions can be successful given the costs involved. Furthermore, we consider that the proper sequencing of the care pathway should be defined according to the following eight phases: (1) Prevention and screening; (2) Signs and symptoms; (3) Early detection; (4) Diagnostic; (5) Referral systems; (6) Treatment; (7) Follow-ups; (8) Reeducation or Palliative care. In this way, the development of recognized KPIs enabling international comparisons of care pathways should finally make it possible to share knowledge and improve care pathways.

According to this definition and based on the literature review, we propose the following integrative conceptual framework illustrated in Fig.  7 .

Integrative conceptual framework of care pathway

Using systematic review, concept analysis and bibliometric analysis, it was possible to develop a detailed understanding of the care pathway concept enabling us to propose an integrative conceptual framework and definition to try to meet the need for an international consensus and thus enabling international comparisons and improvement of care pathways.

The results of our work have highlighted the evolution and advances of the various uses of care pathways. Initially focused more on an organizational approach, there is growing support in the literature for a holistic approach that addresses the entire care across the continuum at the system level [ 4 , 24 , 42 , 60 ]. Thus, patient centeredness has become the primary focus as more and more authors focus on the patient experience as the unit of quality analysis. In doing so, they have given greater importance to social relationships and especially to the relationship as a basic need and highlighted the need to design the service line structures mirroring patients’ needs [ 56 ]. They therefore approach the patient, not only as the individual who follows the pathway, but as a social being who has needs and expectations to fulfill, making meeting the needs and expectations of the patient and caregivers the core of the care pathway [ 24 , 50 , 51 , 57 ]. However, the evaluation of the quality of healthcare services by the patient still raises several methodological questions to finally go beyond the simple consideration of satisfaction. Finally, patient and public involvement and patient engagement are also important issues to the point that some authors see a real power struggle between patients and clinicians [ 53 ] that can lead to tokenistic involvement.

The professional actors involved in the care pathway are naturally essential players, both because of their professional competencies and their ability to orient themselves towards the needs of the patient. However, they are also often part of a neglected factor. Some authors have shown one of the key criteria for the potential failure of care pathways is a failure to take into account the prevailing social dynamics and the importance of the buy-in of all stakeholders [ 65 ]. Moreover, some authors insist on the importance of the actors involved in the pathway to both integrate the social dynamics and confront the patient’s needs with operational realities and organizational constraints [ 24 ].

The operation management of process approach to care delivery also raises many challenges. Thus, some authors have developed tools for modeling and improving care processes by applying them in a systemic approach to incorporate clinical decision support into the modeling method [ 60 ]. This issue of continuous integration of updated guidelines into care pathways is indeed a major challenge given the rapid evolution of knowledge and the limited capacity of professionals to continuously integrate new knowledge. In addition, data simulation and data analysis methods coupled with process improvement methods are undeniable contributions to improve the issue of fluidity of processes and therefore the overall performance [ 49 ]. However, one of the pitfalls of staying focused on the process would be a failure to consider the social dimension, particularly the prevailing social dynamics.

Coordination structures are one of the points of improvement in the systemic approach. Ensuring the continuity of information along the care pathway, as well as having a formal leader for each portion of the pathway, would solve many of the problems of path breaks or unnecessary repetition of exams that cause unnecessary costs [ 5 , 56 , 59 ]. This begins with the implementation of a single information system and the integration of IT infrastructures across the entire care pathway at the system level and accessible to care professionals as well as patients and caregivers [ 4 , 50 , 51 , 65 ].

The structural context of the system and organizations cannot be neglected because it directly impacts the results of the implementation of the care pathway. Firstly, because some physical constraints such as distances between several organizational entities [ 12 , 14 ] can only be solved by major transformations in the infrastructures or in the initial process. Secondly, because failing to consider the dominant social dynamics could immediately call into question the entire care pathway intervention [ 3 , 24 ] by implementing only cosmetic changes and not transforming clinical, administrative and organizational practices in a sustainable manner.

The information system plays a special role in care pathway, not only because it is the support of the informational continuity, but also because it enables real-time data analysis to support decision-making within the care pathway in the form of feedback loops [ 4 , 24 , 51 ].

Finally, it seems clear that care pathway programs at the systemic level are one potential intervention which could benefit from the implementation of a learning system [ 4 ]. Care pathway outcome data can be used as feedback to identify improvement opportunities at various stages of the process or at specific interfaces between stakeholders. This approach makes it possible to support the continuous improvement of the care process.

Given the richness of the contributions of the last 20 years, we advocate an integrated approach resulting in a fine-grained and comprehensive understanding of care pathway. Our proposal is compatible with the definition of Vanhaecht et al. [ 25 ] currently used by the EPA, but in our opinion, enriches it. It allows users to specify the operational realities to which stakeholders should pay attention. Moreover, it insists on adaptation to the social realities and the changes that inevitably accompany it and directly impact the success or failure. However, we were surprised that the approach to managing organizational change and transformation of practices were little addressed. Only Van Citters et al. [ 65 ] had noted that change management approaches were critical for successful care transformation and that they had been largely neglected in care pathways. We share this point of view and believe that care pathway intervention leaders must develop communicative action skills to support practices transformation. Not mentioned in the selected literature, we propose to enrich our conceptual framework of communicative action proposed by Habermas [ 79 ]. From our point of view, this dimension could explain the failures of such interventions or at least the difficulty in developing sustainable transformations in practices.

In general, the concept analysis approach has raised several questions about the depth of concept analysis and its place in knowledge advancement [ 80 ]. However, we believe that the combination of systematic review rigor and concept analysis richness, was necessary to meet the aims of this study and produced an integrated conceptual framework which is ready for use. However, this research has some limitations. Although interest is growing, few studies offer comprehensive empirical results on the deployment of a care pathway and its outcomes in a global systemic approach over the entire continuum of care. Moreover, there are a few examples of in-depth analysis of car pathways over a long period of time. Together, this means that the literature still offers little insight into potential outcomes of care pathways. Lastly, our analysis was limited to peer-reviewed articles; including other contributions such as theses and dissertations as well as grey literature could have brought out other categories or themes.

This study has resulted in a fine-grained understanding of care pathways and in a clear definition relying on a powerful conceptual framework. It responds to a strong need for conceptual precision, as previous reviews have not addressed the care pathway on a systemic scale and in a holistic manner. In addition, our framework offers a holistic view of the pathway without being specific to a particular condition or context. Our framework encompasses 28 subcategories grouped into seven care pathway attributes that should be considered in complex care pathway intervention. It considers both operational and social realities and supporting the improvement and sustainable transformation of clinical, administrative, and organizational practices for the benefit of patients and caregivers, while taking into account professional experience, organizational constraints, and social dynamics. The formulation of these attributes, antecedents as success factors and consequences as potential outcomes, linked to their KPIs, allows the operationalization of this model for any pathway in any context. We believe that these results are of particular interest to policymakers, decision makers, managers and researchers alike, and that they could lead to an international consensus that would finally allow comparison of care pathway improvement programs. However, we consider that the development of a framework for analyzing the performance of such an intervention has yet to be developed in a more in-depth manner, such as by focusing on certain particularities of each phase so that managers and decision makers can rely on validated dashboards and KPIs. More empirical work needs to be done on the comprehensive approach, as defined in our proposed definition, to provide reliable results on the ability of these interventions to result in an overall improvement. In addition, the question of the understanding of social evaluation of the quality of care by the patient remains an open question, as the patient experience does not yet have conclusive KPIs as it is too often limited to patient satisfaction or QALYs.

Availability of data and materials

This systematic review is based on an analysis of 44 published papers which are all referenced within this manuscript. Data supporting our findings are included in the form of additional files.

Abbreviations

European Pathway Association

Institute of Medicine of America

Key Performance Indicator

Preferred Reporting Items for Systematic reviews and Meta-Analyses

Quality Adjusted Life Year

World Health Organization

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Gartner, JB., Abasse, K.S., Bergeron, F. et al. Definition and conceptualization of the patient-centered care pathway, a proposed integrative framework for consensus: a Concept analysis and systematic review. BMC Health Serv Res 22 , 558 (2022). https://doi.org/10.1186/s12913-022-07960-0

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How Inclusive Brands Fuel Growth

• Omar Rodríguez-Vilá,
• Dionne Nickerson,
• Sundar Bharadwaj

Years before the Barbie movie phenomenon, leaders at Mattel became concerned that consumer perceptions of the famous doll were out of sync with demographic trends. The company conducted in-depth research to understand how customers felt about Barbie and to determine whether more-inclusive versions presented a strong market opportunity. The findings led to a new inclusion strategy that affected all areas of the brand—product design, distribution, and commercial activities—and coincided with a period of significant growth. Barbie revenues increased 63% from 2015 to 2022—before the boost from the film.

Research shows that in most industries the perception of inclusion can materially change customers’ likelihood to purchase and willingness to recommend products and services.

This article presents a framework for increasing marketplace inclusion in three areas: seeing the market, which is about market definition, market intelligence, and strategies for growth; serving the market, which involves developing products, packaging, and other commercial practices; and being in the market, which looks at advocacy and the customer experience.

They unlock new sources of value by meeting the needs of underrecognized customers.

Idea in Brief

The opportunity.

Research shows that the perception of inclusion can materially change customers’ likelihood to purchase and willingness to recommend products and services.

The Problem

Despite the many business and societal benefits of marketplace inclusion, there is a systematic lack of it across industries.

The Approach

Greta Gerwig’s Barbie grossed more than $1 billion at the box office in about two weeks. Only 53 films have ever hit that mark (adjusted for inflation). The 2023 movie, which features themes of women’s empowerment, multiculturalism, and inclusiveness, was a divergence from the narrow social and demographic representation of the original tall, thin, white doll that Mattel introduced in 1959.

• OR Omar Rodríguez-Vilá is a professor of marketing practice at the Goizueta Business School at Emory University and the academic director of education at its Business & Society Institute.
• DN Dionne Nickerson is an assistant professor of marketing at the Goizueta Business School.
• SB Sundar Bharadwaj is the Coca-Cola Company Chair of Marketing at the University of Georgia’s Terry College of Business. LinkedIn: Sundar Bharadwaj

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