research frameworks

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Theoretical Framework
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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.

• << Previous: The Research Problem/Question
• Next: 5. The Literature Review >>
• Last Updated: Apr 5, 2024 1:38 PM
• URL: https://libguides.usc.edu/writingguide

Research Framework

• First Online: 20 September 2023

Cite this chapter

• Suneel Kumar 2  

17 Accesses

This section presents the research design, provides a description and justification of the methodological approach and methods used, and details the research framework for the study. In addition, it presents the research objectives and highlights the research hypothesis; discusses about the research area, sampling techniques used, and the sample size drawn for the study; and presents the questionnaire used for collection of data and a detailed view of the statistical tools and techniques used in the study for the analysis purpose.

• Flexible strategies
• Sustainable development goals
• Judgmental sampling
• Snowball sampling
• Interpretive structural modeling
• MICMAC analysis
• Continuity-change matrix
• HML-VDB analysis

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Atsushi, I. 2011. Effects of improving infrastructure quality on business costs: Evidence from firm-level data in eastern europe and central asia. The Developing Economies 49 (02): 121–147.

Article   Google Scholar  

Bhardwaj, P. 2019. Types of sampling in research. Journal of the Practice of Cardiovascular Science 5: 157–163.

Boynton, P.M., and T. Greenhalgh. 2004. Selecting, designing, and developing your questionnaire. BMJ 328 (7451): 1312–1315. https://doi.org/10.1136/bmj.328.7451.1312 .

Butler, R.W. 1980. The concept of a tourist area cycle of evolution: Implications for management of resources. Canadian Geographer/ Le Géographe Canadien 24 (01): 5–12.

Cho, J.Y., and E.H. Lee. 2014. Reducing confusion about grounded theory and qualitative content analysis: Similarities and differences. The Qualitative Report 19 (32): 1–20.

Google Scholar  

Creswell, J.W. 2013. Qualitative inquiry research design, choosing among five approaches . Los Angeles: Sage.

Cuthill, M. 2002. Exploratory research: Citizen participation, local government, and sustainable development in Australia. Sustainable Development 10: 79–89.

Elfil, M., and A. Negida. 2017. Sampling methods in clinical research; an educational review. Emergency(Tehran) 5 (01): 52.

Groenewald, T. 2004. A phenomenological research design illustrated. International Journal of Qualitative Methods 3 (01): 42–51.

Grinnell Jr, R. M., & Unrau, Y. A. 2010. Social work research and evaluation: Foundations of evidence-based practice . Oxford University Press.

Hall, J. 2008. Cross-sectional survey design. In Encyclopedia of survey research methods , ed. P.J. Lavrakas, 173–174. Thousand Oaks: Sage.

Huberman, A.M., and M.B. Miles. 1994. Qualitative data analysis: An expanded . 2nd ed. Thousand Oaks: Sage.

Kerlinger, F. N. 1986. Foundations of Behavioural Research (3rd edn). New York: CBS College Publishing.

Kaurav, R.P.S., J. Kaur, and K. Singh. 2013. Rural tourism: Impact study—an integrated way of development of tourism for India. In Changing paradigms of rural management , ed. R.K. Miryala, 313–320. Hyderabad: Zenon Academic Publishing.

Kulkarni, Prashant B., K. Ravi, and S.B. Patil. 2018. Interpretive structural modeling (ISM) for implementation of green supply chain management in construction sector within Maharashtra. International Research Journal of Engineering and Technology (IRJET) : 2460–2472.

Kumar, Suneel, Navneet Guleria Shekhar, and N. Guleria. 2019. Understanding dynamics of niche tourism consumption through interpretive structure modeling. Saaransh RKG Journal of Management 11 (01): 40–48.

Mandal, A., and S.G. Deshmukh. 1994. Vendor selection using Interpretive Structural Modelling (ISM). International Journal of Operations & Production Management 14 (06): 52–59.

Masoodi, M. 2017. A comparative analysis of two qualitative methods: deciding between grounded theory and phenomenology for your research. Vocational Training: Research and Realities 28 (01): 23–40.

de Mello, A.M., and M. Pedroso. 2018. Applied research articles: Narrowing the gap between research and organizations. Revista de Gestão 25 (04): 338–339.

Mohajan, H.K. 2018. Qualitative research methodology in social sciences and related subjects. Journal of Economic Development, Environment and People 7 (01): 23–48.

Nordin, S. 2005. Tourism of tomorrow: Travel trends and forces of change . European Tourism Research Institute.

Praveenkumar, S. 2015. Tourism marketing and consumer behaviour. Research Journal of Social Science and Management 4 (12): 73–81.

Raj, T., Shankar, R., & Suhaib, M. 2008. An ISM approach for modelling the enablers of flexible manufacturing system: The case for India. International Journal of Production Research 46 (24): 6883–6912.

Rizvi, N.U., S. Kashiramka, S. Singh, and Sushil. 2019. A hierarchical model of the determinants of non-performing assets in banks: An ISM and MICMAC approach. Applied Economics : 1–21.

Saini, V. 2015. Skill development in India: Need, challenges and ways forward. Abhinav National Monthly Refereed Journal of Research in Arts & Education 4 (04): 1–9.

Setia, M.S. 2016. Methodology series module 5: sampling strategies. Indian Journal of Dermatology 61 (05): 505–509.

Shekhar, Suneel, and K. Attri. 2017. Incredible India: SWOT analysis of tourism sector. In Development aspects in tourism and hospitality sector , 175–189. New Delhi: Bharti Publications.

Syed Muhammad, S. K. 2016. Basic guidelines for research: An introductory approach or all disciplines , 1st ed. Bangladesh: Book Zone Publication.

Taherdoost, H. 2016. Sampling methods in research methodology; how to choose a sampling technique for research. International Journal of Academic Research in Management 5 (02): 18–27.

Weiermair, K., M. Peters, and M. Schuckert. 2015. Destination development and the tourist life-cycle: Implications for entrepreneurship in alpine tourism. Tourism Recreation Research 32: 83–93.

Link to SDGs

THE 17 GOALS | Sustainable Development (un.org)

Download references

Author information

Authors and affiliations.

Shaheed Bhagat Singh College, University of Delhi, New Delhi, India

Suneel Kumar

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Kumar, S. (2023). Research Framework. In: Sustainable Rural Tourism in Himalayan Foothills. Springer, Cham. https://doi.org/10.1007/978-3-031-40098-8_3

Download citation

DOI : https://doi.org/10.1007/978-3-031-40098-8_3

Published : 20 September 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-40097-1

Online ISBN : 978-3-031-40098-8

eBook Packages : Earth and Environmental Science Earth and Environmental Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Privacy Policy

Buy Me a Coffee

Home » Methodological Framework – Types, Examples and Guide

Methodological Framework – Types, Examples and Guide

Table of Contents

Methodological Framework

Definition:

Methodological framework is a set of procedures, methods, and tools that guide the research process in a systematic and structured manner. It provides a structure for conducting research, collecting and analyzing data, and drawing conclusions. The framework outlines the steps to be taken in a research project, including the research question, hypothesis, data collection methods, data analysis techniques, and the interpretation of the results.

Types of Methodological Framework

There are different types of methodological frameworks that researchers can use depending on the nature of their research question, the type of data they want to collect, and the research methodology they want to employ. Some common types of methodological frameworks include:

Quantitative Research Framework

This type of framework uses numerical data and statistical analysis to test hypotheses and draw conclusions. It involves the collection of structured data through surveys, experiments, or other quantitative methods.

Qualitative Research Framework

This framework is used to explore complex social phenomena and involves the collection of non-numerical data through methods such as interviews, observation, and document analysis. Qualitative research typically involves the use of open-ended questions and in-depth analysis of data.

Mixed Methods Research Framework

This framework combines quantitative and qualitative research methods to address research questions from multiple angles. It involves collecting both numerical and non-numerical data and using both statistical analysis and interpretive techniques to analyze the data.

Action Research Framework

This framework involves the collaboration between researchers and participants to identify and address practical problems in real-world settings. It involves a cyclical process of planning, action, reflection, and evaluation to improve a specific situation or practice.

Case Study Research Framework

This framework involves the in-depth investigation of a specific case or phenomenon, often using qualitative methods. It aims to understand the complexity of the case and draw generalizations from the findings.

How to Develop a Methodological Framework

Developing a methodological framework involves a series of steps that help to guide the research process in a systematic and structured manner. Here are the general steps involved in developing a methodological framework:

• Define the research problem: The first step is to clearly define the research problem or question. This involves identifying the purpose of the research, the research objectives, and the scope of the study.
• Select an appropriate research methodology: The research methodology selected should align with the research problem and research question. Common research methodologies include quantitative, qualitative, mixed-methods, case study, or action research.
• Develop the research design: Once the research methodology is selected, the research design should be developed. This involves identifying the data collection methods, sampling strategy, and data analysis techniques.
• Identify and justify the data collection methods: The data collection methods should be chosen based on the research methodology and research design. For example, if the research methodology is qualitative, data collection methods such as interviews, observation, or document analysis may be used.
• Identify and justify the data analysis techniques: The data analysis techniques should also be chosen based on the research methodology and research design. For quantitative research, this may include statistical analysis techniques, while for qualitative research, this may include interpretive techniques such as thematic analysis.
• Consider ethical considerations: Ethical considerations should be taken into account throughout the research process. This includes obtaining informed consent, ensuring confidentiality and privacy, and protecting the rights of participants.
• Identify potential limitations: It is important to identify potential limitations or biases that may affect the research findings. This includes discussing potential sources of error or bias in the research design, data collection methods, or data analysis techniques.
• Consider the significance and implications of the research: The significance and implications of the research findings should be considered, including their potential contributions to theory, practice, or policy.
• Refine the framework: The methodological framework should be refined based on feedback from peers, experts, or other stakeholders. This involves identifying any areas for improvement in the research design, data collection methods, or data analysis techniques.

Applications of Methodological Framework

Here are some examples of how a methodological framework can be applied in various fields:

• Social sciences: In social sciences, a methodological framework can be used to conduct research on various topics, such as psychology, sociology, and anthropology. For example, a researcher may use a qualitative research methodology to investigate the experiences and perceptions of individuals living in poverty.
• Natural sciences: In natural sciences, a methodological framework can be used to conduct research on various topics, such as biology, chemistry, and physics. For example, a researcher may use a quantitative research methodology to investigate the effects of different fertilizers on crop yield.
• Engineering : In engineering, a methodological framework can be used to design and test new technologies or systems. For example, a researcher may use a mixed-methods research methodology to investigate the usability and effectiveness of a new software application.
• Business : In business, a methodological framework can be used to conduct research on various topics, such as marketing, management, and finance. For example, a researcher may use a quantitative research methodology to investigate the relationship between customer satisfaction and customer loyalty.

When to use Methodological Framework

Here are some specific situations when a methodological framework can be particularly useful:

• When conducting original research: If you are conducting original research, a methodological framework can help ensure that your study is designed in a structured and systematic manner, which increases the reliability and validity of the findings.
• When conducting a literature review: A methodological framework can be used when conducting a literature review to ensure that the review is conducted in a structured and systematic manner. This helps to identify relevant studies and synthesize the findings from multiple studies.
• When replicating previous research: If you are replicating previous research, a methodological framework can help ensure that the replication is conducted in a rigorous and systematic manner. This helps to ensure that the findings are consistent with the original study.
• When developing a research proposal : A methodological framework can be used when developing a research proposal to ensure that the proposal is designed in a structured and systematic manner. This helps to convince reviewers that the study is well-designed and likely to produce valid and reliable findings.
• When teaching research methods: A methodological framework can be used when teaching research methods to provide students with a structured approach to designing and conducting research. This helps to ensure that students understand the research process and are able to conduct research in a rigorous and systematic manner.

Examples of Methodological Framework

Here are some real-time examples of how methodological frameworks are used in various fields:

• In healthcare research, a mixed-methods research framework can be used to evaluate the effectiveness of a new treatment approach. The quantitative component may involve measuring the changes in patient outcomes, while the qualitative component may involve interviewing patients and healthcare providers to understand their perspectives on the treatment.
• In engineering, a design science research framework can be used to develop and test a new software application. The researchers may identify a problem with existing software, develop a new solution, and test it in a real-world setting.
• In business, a case study research framework can be used to understand the impact of a new marketing strategy on a particular company. The researcher may analyze data from the company’s financial statements, conduct interviews with key stakeholders, and observe the implementation of the strategy in order to understand its effectiveness.
• In education, an action research framework can be used to improve teaching practices. A teacher may identify a problem in their classroom, develop a plan to address the problem, implement the plan, and reflect on the results in order to improve their teaching practices.
• In social science research, a grounded theory framework can be used to develop a theory from qualitative data. A researcher may collect data from interviews or observations and use that data to develop a theory about a particular phenomenon.

Purpose of Methodological Framework

The purpose of a methodological framework is to provide a structured and systematic approach to designing, conducting, and analyzing research. The framework serves as a guide for researchers to follow, ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable, valid, and generalizable. Some key purposes of a methodological framework are:

• To provide a clear and concise description of the research process: The framework outlines the steps involved in conducting the research, including the research question, data collection methods, data analysis, and interpretation of results.
• To ensure that the research is conducted in a systematic and rigorous manner : The framework provides a structured approach to the research, helping to ensure that the research is conducted in a way that minimizes bias and maximizes the accuracy and reliability of the results.
• To improve the quality of the research: The framework helps to ensure that the research is of high quality and meets the standards of the field. This can help to increase the impact and relevance of the research.
• To increase transparency and replicability: The framework provides a clear and transparent description of the research process, making it easier for others to understand and replicate the research.
• To facilitate communication and collaboration: The framework provides a common language and structure for researchers to communicate their research findings and collaborate with others in the field.

Characteristics of Methodological Framework

Here are some common characteristics of a methodological framework:

• Systematic : A methodological framework is a systematic approach to research that provides a clear and structured guide for researchers to follow. It outlines the steps involved in conducting research, from developing a research question to analyzing and interpreting data.
• Transparent : A methodological framework promotes transparency in research by providing a clear and concise description of the research process. This helps to ensure that others can understand and replicate the research.
• Flexible : A methodological framework should be flexible enough to accommodate different research designs and methodologies. It should allow for modifications based on the specific research question, data collection methods, and analysis techniques.
• Contextual : A methodological framework should take into account the contextual factors that may impact the research. This includes the cultural, social, and historical context of the research, as well as the research setting and the characteristics of the participants.
• Rigorous : A methodological framework promotes rigor in research by ensuring that the research is conducted in a systematic and unbiased manner. It includes strategies for minimizing bias and ensuring the validity and reliability of the findings.
• Theory-driven: A methodological framework should be grounded in theoretical concepts and principles that guide the research. This helps to ensure that the research is relevant and meaningful, and that the findings can be applied to broader theoretical frameworks.

Advantages of Methodological Framework

There are several advantages to using a methodological framework in research:

• Structured approach: A methodological framework provides a clear and structured approach to conducting research, which helps to ensure that the research is conducted in a systematic and rigorous manner.
• Increased efficiency: A methodological framework can increase the efficiency of the research process by providing a clear roadmap for researchers to follow, reducing the time and resources required to conduct the research.
• Reproducibility: A methodological framework promotes reproducibility by providing a clear and transparent description of the research process, making it easier for others to replicate the research.
• Improved quality : A methodological framework can improve the quality of research by ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable and valid.
• Standardization : A methodological framework promotes standardization in research, helping to ensure that the research meets the standards of the field and is comparable to other research studies.
• Better communication : A methodological framework provides a common language and structure for researchers to communicate their research findings, facilitating communication and collaboration among researchers.
• Theory development: A methodological framework can contribute to the development of theory by providing a structured approach to data collection and analysis that is grounded in theoretical concepts and principles.

Limitations of Methodological Framework

While there are many advantages to using a methodological framework in research, there are also some limitations to be aware of:

• Flexibility : While a methodological framework can provide a structured approach to research, it may also limit flexibility in the research process. Researchers may feel constrained by the framework and unable to deviate from the prescribed steps, which may limit their ability to adapt to unexpected findings or changes in the research context.
• Applicability : Methodological frameworks may not be equally applicable to all research questions and contexts. Some frameworks may be more suitable for certain types of research than others, and researchers may need to modify or adapt the framework to fit their specific research question and context.
• Complexity : Some methodological frameworks can be complex and difficult to understand, particularly for novice researchers. This may limit their usefulness in certain contexts or for certain types of research.
• Time and resource constraints : Using a methodological framework may require additional time and resources to fully implement, which may not be feasible for all researchers or research projects.
• Overemphasis on methodology: While a methodological framework can provide a structured approach to research methodology, it may overemphasize the importance of methodology over other aspects of research, such as theoretical frameworks or ethical considerations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Cluster Analysis – Types, Methods and Examples

Discriminant Analysis – Methods, Types and...

MANOVA (Multivariate Analysis of Variance) –...

Documentary Analysis – Methods, Applications and...

ANOVA (Analysis of variance) – Formulas, Types...

Graphical Methods – Types, Examples and Guide

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Research paper

What Is a Theoretical Framework? | Guide to Organizing

Published on October 14, 2022 by Sarah Vinz . Revised on November 20, 2023 by Tegan George.

A theoretical framework is a foundational review of existing theories that serves as a roadmap for developing the arguments you will use in your own work.

Theories are developed by researchers to explain phenomena, draw connections, and make predictions. In a theoretical framework, you explain the existing theories that support your research, showing that your paper or dissertation topic is relevant and grounded in established ideas.

In other words, your theoretical framework justifies and contextualizes your later research, and it’s a crucial first step for your research paper , thesis , or dissertation . A well-rounded theoretical framework sets you up for success later on in your research and writing process.

Table of contents

Why do you need a theoretical framework, how to write a theoretical framework, structuring your theoretical framework, example of a theoretical framework, other interesting articles, frequently asked questions about theoretical frameworks.

Before you start your own research, it’s crucial to familiarize yourself with the theories and models that other researchers have already developed. Your theoretical framework is your opportunity to present and explain what you’ve learned, situated within your future research topic.

There’s a good chance that many different theories about your topic already exist, especially if the topic is broad. In your theoretical framework, you will evaluate, compare, and select the most relevant ones.

By “framing” your research within a clearly defined field, you make the reader aware of the assumptions that inform your approach, showing the rationale behind your choices for later sections, like methodology and discussion . This part of your dissertation lays the foundations that will support your analysis, helping you interpret your results and make broader generalizations .

• In literature , a scholar using postmodernist literary theory would analyze The Great Gatsby differently than a scholar using Marxist literary theory.
• In psychology , a behaviorist approach to depression would involve different research methods and assumptions than a psychoanalytic approach.
• In economics , wealth inequality would be explained and interpreted differently based on a classical economics approach than based on a Keynesian economics one.

To create your own theoretical framework, you can follow these three steps:

• Identifying your key concepts
• Evaluating and explaining relevant theories
• Showing how your research fits into existing research

1. Identify your key concepts

The first step is to pick out the key terms from your problem statement and research questions . Concepts often have multiple definitions, so your theoretical framework should also clearly define what you mean by each term.

To investigate this problem, you have identified and plan to focus on the following problem statement, objective, and research questions:

Problem : Many online customers do not return to make subsequent purchases.

Objective : To increase the quantity of return customers.

Research question : How can the satisfaction of company X’s online customers be improved in order to increase the quantity of return customers?

2. Evaluate and explain relevant theories

By conducting a thorough literature review , you can determine how other researchers have defined these key concepts and drawn connections between them. As you write your theoretical framework, your aim is to compare and critically evaluate the approaches that different authors have taken.

After discussing different models and theories, you can establish the definitions that best fit your research and justify why. You can even combine theories from different fields to build your own unique framework if this better suits your topic.

Make sure to at least briefly mention each of the most important theories related to your key concepts. If there is a well-established theory that you don’t want to apply to your own research, explain why it isn’t suitable for your purposes.

3. Show how your research fits into existing research

Apart from summarizing and discussing existing theories, your theoretical framework should show how your project will make use of these ideas and take them a step further.

You might aim to do one or more of the following:

• Test whether a theory holds in a specific, previously unexamined context
• Use an existing theory as a basis for interpreting your results
• Critique or challenge a theory
• Combine different theories in a new or unique way

A theoretical framework can sometimes be integrated into a literature review chapter , but it can also be included as its own chapter or section in your dissertation. As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

There are no fixed rules for structuring your theoretical framework, but it’s best to double-check with your department or institution to make sure they don’t have any formatting guidelines. The most important thing is to create a clear, logical structure. There are a few ways to do this:

• Draw on your research questions, structuring each section around a question or key concept
• Organize by theory cluster
• Organize by date

It’s important that the information in your theoretical framework is clear for your reader. Make sure to ask a friend to read this section for you, or use a professional proofreading service .

As in all other parts of your research paper , thesis , or dissertation , make sure to properly cite your sources to avoid plagiarism .

To get a sense of what this part of your thesis or dissertation might look like, take a look at our full example .

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
• Self-serving bias
• Availability heuristic
• Halo effect
• Hindsight bias
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Vinz, S. (2023, November 20). What Is a Theoretical Framework? | Guide to Organizing. Scribbr. Retrieved April 7, 2024, from https://www.scribbr.com/dissertation/theoretical-framework/

Is this article helpful?

Sarah's academic background includes a Master of Arts in English, a Master of International Affairs degree, and a Bachelor of Arts in Political Science. She loves the challenge of finding the perfect formulation or wording and derives much satisfaction from helping students take their academic writing up a notch.

Other students also liked

What is a research methodology | steps & tips, how to write a literature review | guide, examples, & templates, what is a conceptual framework | tips & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Theories, Models, & Frameworks

One of the cornerstones of implementation science is the use of theory.

Unfortunately, the vast number of theories, models, and frameworks available in the implementation science toolkit can make it difficult to determine which is the most appropriate to address or frame a research question. There are dozens of theories, models, and frameworks used in implementation science that have been developed across a wide range of disciplines, and more are published each year.

Two reviews provide schemas to organize implementation science theories, models, and frameworks and narrow the range of choices:

Bridging research and practice: models for dissemination and implementation research (Tabak, Khoong, Chambers, & Brownson, 2013) Tabak et al’s schema organizes 61 dissemination and implementation models based on three variables: 1) construct flexibility, 2) focus on dissemination and/or implementation activities, and 3) socio-ecological framework level.

Doing Research

Frame your question, ⇥ pick a theory, model, or framework, identify implementation strategies, select research method, select study design, choose measures, get funding, report results.

The authors argue that classification of a model based on these three variables will assist in selecting a model to inform D&I science study design and execution. For more information, check out this archived NCI webinar with presenters Dr. Rachel Tabak and Dr. Ted Skolarus: 💻 Applying Models and Frameworks to D&I Research: An Overview & Analysis .

✪ Making sense of implementation theories, models, and frameworks (Nilsen, 2015) Per Nilsen's schema sorts implementation science theories, models, and frameworks into five categories: 1) process models, 2) determinants frameworks, 3) classic theories, 4) implementation theories, and 5) evaluation frameworks.

Adapted from: Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci . 2015;10(1):1-13.

Below, we borrow from Nilsen’s schema to organize overviews of a selection of implementation science theories, models, and frameworks. In each overview, you will find links to additional resources.

Open Access articles will be marked with ✪ Please note some journals will require subscriptions to access a linked article.

What are you using implementation science to accomplish.

• To describe or guide the process of translating research into practice
• To understand and/or explain what influences implementation outcomes
• To evaluate implementation

Process Models

Examples of use.

• ✪ Results-based aid with lasting effects: Sustainability in the Salud Mesoamérica Initiative ( Globalization and Health , 2018)
• ✪ Study Protocol: A Clinical Trial for Improving Mental Health Screening for Aboriginal and Torres Strait Islander Pregnant Women and Mothers of Young Children Using the Kimberley Mum's Mood Scale ( BMC Public Health , 2019)
• ✪ Sustainability of Public Health Interventions: Where Are the Gaps? ( Health Research Policy and Systems , 2019)

In 2018 the authors refined the EPIS model into the cyclical EPIS Wheel, allowing for closer alignment with rapid-cycle testing. A model for rigorously applying the Exploration, Preparation, Implementation, Sustainment (EPIS) framework in the design and measurement of a large scale collaborative multi-site study is available Open Access (✪) from Health & Justice .

• ✪ Systematic review of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework ( Implementation Science , 2019)
• A Review of Studies on the System-Wide Implementation of Evidence-Based Psychotherapies for Posttraumatic Stress Disorder in the Veterans Health Administration ( Administration and Policy in Mental Health and Mental Health Services Research , 2016)
• Advancing Implementation Research and Practice in Behavioral Health Systems ( Administration and Policy in Mental Health and Mental Health Services Research , 2016)
• ✪ A model for rigorously applying the Exploration, Preparation, Implementation, Sustainment (EPIS) framework in the design and measurement of a large scale collaborative multi-site study ( Health and Justice , 2018)
• Characterizing Shared and Unique Implementation Influences in Two Community Services Systems for Autism: Applying the EPIS Framework to Two Large-Scale Autism Intervention Community Effectiveness Trials ( Administration and Policy in Mental Health and Mental Health Services Research , 2019)
• 💻 WEBINAR: Use of theory in implementation research: The EPIS framework: A phased and multilevel approach to implementation
• ✪ A two-way street: bridging implementation science and cultural adaptations of mental health treatments ( Implementation Science , 2013)
• ✪ “Scaling-out” evidence-based interventions to new populations or new health care delivery systems ( Implementation Science , 2017)
• ✪ Implementing measurement based care in community mental health: a description of tailored and standardized methods ( Implementation Science , 2018)
• ✪ "I Had to Somehow Still Be Flexible": Exploring Adaptations During Implementation of Brief Cognitive Behavioral Therapy in Primary Care ( Implementation Science , 2018)
• An Implementation Science Approach to Antibiotic Stewardship in Emergency Departments and Urgent Care Centers ( Academic Emergency Medicine , 2020)
• Using the Practical, Robust Implementation and Sustainability Model (PRISM) to Qualitatively Assess Multilevel Contextual Factors to Help Plan, Implement, Evaluate, and Disseminate Health Services Programs ( Translational Behavioral Medicine , 2019)
• Stakeholder Perspectives on Implementing a Universal Lynch Syndrome Screening Program: A Qualitative Study of Early Barriers and Facilitators ( Genetics Medicine , 2016)
• Evaluating the Implementation of Project Re-Engineered Discharge (RED) in Five Veterans Health Administration (VHA) Hospitals ( The Joint Commission Journal on Quality and Patient Safety , 2018)

In 2012 Meyers, Durlak, and Wandersman synthesized information from 25 implementation frameworks with a focus on identifying specific actions that improve the quality of implementation efforts. The result of this synthesis was the Quality Implementation Framework (QIF) , published in the American Journal of Community Psychology . This framework is comprised of fourteen critical steps across four phases of implementation, and has been used widely in child and family services, behavioral health, and hospital settings.

• ✪ Practical Implementation Science: Developing and Piloting the Quality Implementation Tool ( American Journal of Community Psychology , 2012)
• Survivorship Care Planning in a Comprehensive Cancer Center Using an Implementation Framework ( The Journal of Community and Supportive Oncology , 2016)
• ✪ The Application of an Implementation Science Framework to Comprehensive School Physical Activity Programs: Be a Champion! ( Frontiers in Public Health , 2017)
• ✪ Developing and Evaluating a Lay Health Worker Delivered Implementation Intervention to Decrease Engagement Disparities in Behavioural Parent Training: A Mixed Methods Study Protocol ( BMJ Open , 2019)
• Implementation Process and Quality of a Primary Health Care System Improvement Initiative in a Decentralized Context: A Retrospective Appraisal Using the Quality Implementation Framework ( The International Journal of Health Planning and Management , 2019)

Determinant Frameworks

Learn more:.

• Statewide Implementation of Evidence-Based Programs ( Exceptional Children , 2013)
• Active Implementation Frameworks for Successful Service Delivery: Catawba County Child Wellbeing Project ( Research on Social Work Practice , 2014)
• The Active Implementation Frameworks: A roadmap for advancing implementation of Comprehensive Medication Management in primary care ( Research in Social and Administrative Pharmacy , 2017)

For additional resources, please visit the CFIR Technical Assistance Website . The website has tools and templates for studying implementation of innovations using the CFIR framework, and these tools can help you learn more about issues pertaining to inner and outer contexts. You can read the original framework development article in the Open Access (✪) journal Implementation Science .

• ✪ Evaluating and Optimizing the Consolidated Framework for Implementation Research (CFIR) for use in Low- and Middle-Income Countries: A Systematic Review ( Implementation Science , 2020)
• ✪ A systematic review of the use of the Consolidated Framework for Implementation Research ( Implementation Science , 2017)
• Using the Consolidated Framework for Implementation Research (CFIR) to produce actionable findings: A rapid-cycle evaluation approach to improving implementation ( Implementation Science , 2017)
• ✪ The Consolidated Framework for Implementation Research: Advancing implementation science through real-world applications, adaptations, and measurement ( Implementation Science , 2015)
• 💻 WEBINAR: Use of theory in implementation research: Pragmatic application and scientific advancement of the Consolidated Framework for Implementation Research (CFIR) (Dr. Laura Damschroder, National Cancer Institute of NIH Fireside Chat Series )

In 2005, Dr. Susan Michie and colleagues published the Theoretical Domains Framework in BMJ Quality & Safety , the result of a consensus process to develop a theoretical framework for implementation research. The primary goals of the development team were to determine key theoretical constructs for studying evidence based practice implementation and for developing strategies for effective implementation, and for these constructs to be accessible and meaningful across disciplines.

• ✪ Validation of the theoretical domains framework for use in behaviour change and implementation research ( Implementation Science , 2012)
• ✪ Theoretical domains framework to assess barriers to change for planning health care quality interventions: a systematic literature review ( Journal of Multidisciplinary Healthcare , 2016)
• ✪ Combined use of the Consolidated Framework for Implementation Research (CFIR) and the Theoretical Domains Framework (TDF): a systematic review ( Implementation Science , 2017)
• ✪ Applying the Theoretical Domains Framework to identify barriers and targeted interventions to enhance nurses’ use of electronic medication management systems in two Australian hospitals ( Implementation Science , 2017)
• ✪ A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems ( Implementation Science , 2017)
• ✪ Hospitals Implementing Changes in Law to Protect Children of Ill Parents: A Cross-Sectional Study ( BMC Health Services Research , 2018)
• Addressing the Third Delay: Implementing a Novel Obstetric Triage System in Ghana ( BMJ Global Health , 2018)

The original framework development article, Enabling the implementation of evidence based practice: a conceptual framework is available Open Access (✪) from BMJ Quality & Safety .

• Ingredients for change: revisiting a conceptual framework ( BMJ Quality & Safety , 2002)
• Evaluating the successful implementation of evidence into practice using the PARIHS framework: theoretical and practical challenges ( Implementation Science , 2008)
• ✪ A critical synthesis of literature on the promoting action on research implementation in health services (PARIHS) framework ( Implementation Science , 2010)
• ✪ A Guide for applying a revised version of the PARIHS framework for implementation ( Implementation Science , 2011)
• 💻 WEBINAR: Use of theory in implementation research; Pragmatic application and scientific advancement of the Promoting Action on Research Implementation in Health Services (PARiHS) framework

Classic Theories

In 2017 Dr. Sarah Birken and colleagues published their application of four organizational theories to published accounts of evidence-based program implementation. The objective was to determine whether these theories could help explain implementation success by shedding light on the impact of the external environment on the implementing organizations.

Their paper, ✪ Organizational theory for dissemination and implementation research , published in the journal Implementation Science utilized transaction cost economics theory , institutional theory , contingency theories , and resource dependency theory for this work.

In 2019, Dr. Jennifer Leeman and colleagues applied these same three organizational theories to case studies of the implementation of colorectal cancer screening interventions in Federally Qualified Health Centers, in ✪ Advancing the use of organization theory in implementation science ( Preventive Medicine , 2019).

In 2005 the NIH published ✪ Theory at a Glance: A Guide For Health Promotion Practice 2.0, an overview of behavior change theories. Below are selected theories from the intrapersonal and interpersonal ecological levels most relevant to implementation science.

There are two intrapersonal behavioral theories most often used to interpret individual behavior variation:

The Health Belief Model : An initial theory of health behavior, the HBM arose from work in the 1950s by a group of social psychologists in the U.S. wishing to understand why health improvement services were not being used. The HBM posited that in the health behavior context, readiness to act arises from six factors: perceived susceptibility , perceived severity . perceived benefits , perceived barriers , a cue to action , and self-efficacy . To learn more about the Health Belief Model, please read "Historical Origins of the Health Belief Model" ( Health Education Monographs ).

The Theory of Planned Behavior : This theory, developed by Ajzen in the late 1980s and formalized in 1991 , sees the primary driver of behavior as being behavioral intention . Through the lens of the TPB, behavioral intention is believed to be influenced by an individual's attitude , their perception of peers' subjective norms , and the individual's perceived behavioral control .

At the interpersonal behavior level , where individual behavior is influenced by a social environment, Social Cognitive Theory is the most widely used theory in health behavior research.

Social Cognitive Theory : Published by Bandera in the 1978 article, Self-efficacy: Toward a unifying theory of behavioral change , SCT consists of six main constructs: reciprocal determinism , behavioral capability , expectations , observational learning , reinforcements , and self-efficacy (which is seen as the most important personal factor in changing behavior).

Examples of use in implementation science:

The Health Belief Model

• ✪ Using technology for improving population health: comparing classroom vs. online training for peer community health advisors in African American churches ( Implementation Science , 2015)

The Theory of Planned Behavior

• ✪ Assessing mental health clinicians’ intentions to adopt evidence-based treatments: reliability and validity testing of the evidence-based treatment intentions scale ( Implementation Science , 2016)

Social Cognitive Theory

• ✪ Systematic development of a theory-informed multifaceted behavioural intervention to increase physical activity of adults with type 2 diabetes in routine primary care: Movement as Medicine for Type 2 Diabetes ( Implementation Science , 2016)
• Diffusion of preventive innovations ( Addictive Behaviors , 2002)
• ✪ Diffusion of Innovation Theory ( Canadian Journal of Nursing Informatics , 2011)

Implementation Theories

• ✪ Implementing community-based provider participation in research: an empirical study ( Implementation Science , 2012)
• ✪ Context matters: measuring implementation climate among individuals and groups ( Implementation Science , 2014)
• ✪ Determining the predictors of innovation implementation in healthcare: a quantitative analysis of implementation effectiveness ( BMC Health Services Research , 2015)
• Review: Conceptualization and Measurement of Organizational Readiness for Change ( Medical Care Research and Review , 2008)
• ✪ Organizational factors associated with readiness to implement and translate a primary care based telemedicine behavioral program to improve blood pressure control: the HTN-IMPROVE study ( Implementation Science , 2013)
• ✪ Towards evidence-based palliative care in nursing homes in Sweden: a qualitative study informed by the organizational readiness to change theory ( Implementation Science , 2018)
• ✪ Assessing the reliability and validity of the Danish version of Organizational Readiness for Implementing Change (ORIC) ( Implementation Science , 2018)
• ✪ Development of a theory of implementation and integration: Normalization Process Theory ( Implementation Science , 2009)
• ✪ Implementation, context and complexity ( Implementation Science , 2016)
• ✪ Exploring the implementation of an electronic record into a maternity unit: a qualitative study using Normalisation Process Theory ( BMC Medical Informatics and Decision Making , 2017)
• ✪ Implementation of cardiovascular disease prevention in primary health care: enhancing understanding using normalisation process theory ( BMC Family Practice , 2017)
• ✪ Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review ( Implementation Science , 2018)

Evaluation Frameworks

The framework development article, ✪ Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda , is available through Administration and Policy in Mental Health and Mental Health Services Research .

In 2023, Dr. Proctor and several colleagues published Ten years of implementation outcomes research: a scoping review in the journal Implementation Science , a scoping review of 'the field’s progress in implementation outcomes research.'

• Toward Evidence-Based Measures of Implementation: Examining the Relationship Between Implementation Outcomes and Client Outcomes ( Journal of Substance Abuse Treatment , 2016)
• ✪ Toward criteria for pragmatic measurement in implementation research and practice: a stakeholder-driven approach using concept mapping ( Implementation Science , 2017)
• ✪ German language questionnaires for assessing implementation constructs and outcomes of psychosocial and health-related interventions: a systematic review ( Implementation Science , 2018)
• The Elusive Search for Success: Defining and Measuring Implementation Outcomes in a Real-World Hospital Trial ( Frontiers In Public Health , 2019)

In 1999, authors Glasgow, Vogt, and Boles developed this framework because they felt tightly controlled efficacy studies weren’t very helpful in informing program scale-up or in understanding actual public health impact of an intervention. The RE-AIM framework has been refined over time to guide the design and evaluation of complex interventions in order to maximize real-life public health impact.

This framework helps researchers collect information needed to translate research to effective practice, and may also be used to guide implementation and potential scale-up activities. You can read the original framework development article in The American Journal of Public Health . Additional resources, support, and publications on the RE-AIM framework can be found at RE-AIM.org . The 2021 special issue of Frontiers in Public Health titled Use of the RE-AIM Framework: Translating Research to Practice with Novel Applications and Emerging Directions includes more than 20 articles on RE-AIM.

• What Does It Mean to “Employ” the RE-AIM Model? ( Evaluation & the Health Professions , 2012)
• The RE-AIM Framework: A Systematic Review of Use Over Time (The American Journal of Public Health , 2013)
• ✪ Fidelity to and comparative results across behavioral interventions evaluated through the RE-AIM framework: a systematic review ( Systematic Reviews , 2015)
• ✪ Qualitative approaches to use of the RE-AIM framework: rationale and methods ( BMC Health Services Research , 2018)
• ✪ RE-AIM in Clinical, Community, and Corporate Settings: Perspectives, Strategies, and Recommendations to Enhance Public Health Impact ( Frontiers in Public Health , 2018)
• ✪ RE-AIM Planning and Evaluation Framework: Adapting to New Science and Practice With a 20-Year Review ( Frontiers in Public Health , 2019)
• ✪ RE-AIM in the Real World: Use of the RE-AIM Framework for Program Planning and Evaluation in Clinical and Community Settings ( Frontiers in Public Health , 2019)

Be boundless

Connect with us:.

© 2024 University of Washington | Seattle, WA

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 4: Theoretical frameworks for qualitative research

Tess Tsindos

Learning outcomes

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

• Describe qualitative frameworks.
• Explain why frameworks are used in qualitative research.
• Identify various frameworks used in qualitative research.

What is a Framework?

A framework is a set of broad concepts or principles used to guide research.  As described by Varpio and colleagues 1 , a framework is a logically developed and connected set of concepts and premises – developed from one or more theories – that a researcher uses as a scaffold for their study. The researcher must define any concepts and theories that will provide the grounding for the research and link them through logical connections, and must relate these concepts to the study that is being carried out. In using a particular theory to guide their study, the researcher needs to ensure that the theoretical framework is reflected in the work in which they are engaged.

It is important to acknowledge that the terms ‘theories’ ( see Chapter 3 ), ‘frameworks’ and ‘paradigms’ are sometimes used interchangeably. However, there are differences between these concepts. To complicate matters further, theoretical frameworks and conceptual frameworks are also used. In addition, quantitative and qualitative researchers usually start from different standpoints in terms of theories and frameworks.

A diagram by Varpio and colleagues demonstrates the similarities and differences between theories and frameworks, and how they influence research approaches. 1(p991) The diagram displays the objectivist or deductive approach to research on the left-hand side. Note how the conceptual framework is first finalised before any research is commenced, and it involves the articulation of hypotheses that are to be tested using the data collected. This is often referred to as a top-down approach and/or a general (theory or framework) to a specific (data) approach.

The diagram displays the subjectivist or inductive approach to research on the right-hand side. Note how data is collected first, and through data analysis, a tentative framework is proposed. The framework is then firmed up as new insights are gained from the data analysis. This is referred to as a specific (data) to general (theory and framework) approach .

Why d o w e u se f rameworks?

A framework helps guide the questions used to elicit your data collection. A framework is not prescriptive, but it needs to be suitable for the research question(s), setting and participants. Therefore, the researcher might use different frameworks to guide different research studies.

A framework informs the study’s recruitment and sampling, and informs, guides or structures how data is collected and analysed. For example, a framework concerned with health systems will assist the researcher to analyse the data in a certain way, while a framework concerned with psychological development will have very different ways of approaching the analysis of data. This is due to the differences underpinning the concepts and premises concerned with investigating health systems, compared to the study of psychological development. The framework adopted also guides emerging interpretations of the data and helps in comparing and contrasting data across participants, cases and studies.

Some examples of foundational frameworks used to guide qualitative research in health services and public health:

• The Behaviour Change Wheel 2
• Consolidated Framework for Implementation Research (CFIR) 3
• Theoretical framework of acceptability 4
• Normalization Process Theory 5
• Candidacy Framework 6
• Aboriginal social determinants of health 7(p8)
• Social determinants of health 8
• Social model of health 9,10
• Systems theory 11
• Biopsychosocial model 12
• Discipline-specific models
• Disease-specific frameworks

E xamples of f rameworks

In Table 4.1, citations of published papers are included to demonstrate how the particular framework helps to ‘frame’ the research question and the interpretation of results.

Table 4.1. Frameworks and references

As discussed in Chapter 3, qualitative research is not an absolute science. While not all research may need a framework or theory (particularly descriptive studies, outlined in Chapter 5), the use of a framework or theory can help to position the research questions, research processes and conclusions and implications within the relevant research paradigm. Theories and frameworks also help to bring to focus areas of the research problem that may not have been considered.

• Varpio L, Paradis E, Uijtdehaage S, Young M. The distinctions between theory, theoretical framework, and conceptual framework. Acad Med . 2020;95(7):989-994. doi:10.1097/ACM.0000000000003075
• Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci .  2011;6:42. doi:10.1186/1748-5908-6-42
• CFIR Research Team. Consolidated Framework for Implementation Research (CFIR). Center for Clinical Management Research. 2023. Accessed February 15, 2023. https://cfirguide.org/
• Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res . 2017;17:88. doi:10.1186/s12913-017-2031-8
• Murray E, Treweek S, Pope C, et al. Normalisation process theory: a framework for developing, evaluating and implementing complex interventions. BMC Med .  2010;8:63. doi:10.1186/1741-7015-8-63
• Tookey S, Renzi C, Waller J, von Wagner C, Whitaker KL. Using the candidacy framework to understand how doctor-patient interactions influence perceived eligibility to seek help for cancer alarm symptoms: a qualitative interview study. BMC Health Serv Res . 2018;18(1):937. doi:10.1186/s12913-018-3730-5
• Lyon P. Aboriginal Health in Aboriginal Hands: Community-Controlled Comprehensive Primary Health Care @ Central Australian Aboriginal Congress; 2016. Accessed February 15, 2023. https://nacchocommunique.com/wp-content/uploads/2017/09/cphc-congress-final-report.pdf
• Solar O., Irwin A. A Conceptual Framework for Action on the Social Determinants of Health:   Social Determinants of Health Discussion Paper 2 (Policy and Practice); 2010. Accessed February 22, 2023. https://www.who.int/publications/i/item/9789241500852
• Yuill C, Crinson I, Duncan E. Key Concepts in Health Studies . SAGE Publications; 2010.
• Germov J. Imagining health problems as social issues. In: Germov J, ed. Second Opinion: An Introduction to Health Sociology . Oxford University Press; 2014.
• Laszlo A, Krippner S. Systems theories: their origins, foundations, and development. In: Jordan JS, ed. Advances in Psychology . Science Direct; 1998:47-74.
• Engel GL. From biomedical to biopsychosocial: being scientific in the human domain. Psychosomatics . 1997;38(6):521-528. doi:10.1016/S0033-3182(97)71396-3
• Schmidtke KA, Drinkwater KG. A cross-sectional survey assessing the influence of theoretically informed behavioural factors on hand hygiene across seven countries during the COVID-19 pandemic. BMC Public Health . 2021;21:1432. doi:10.1186/s12889-021-11491-4
• Graham-Wisener L, Nelson A, Byrne A, et al. Understanding public attitudes to death talk and advance care planning in Northern Ireland using health behaviour change theory: a qualitative study. BMC Public Health . 2022;22:906. doi:10.1186/s12889-022-13319-1
• Walker R, Quong S, Olivier P, Wu L, Xie J, Boyle J. Empowerment for behaviour change through social connections: a qualitative exploration of women’s preferences in preconception health promotion in the state of Victoria, Australia. BMC Public Health . 2022;22:1642. doi:10.1186/s12889-022-14028-5
• Ayton DR, Barker AL, Morello RT, et al. Barriers and enablers to the implementation of the 6-PACK falls prevention program: a pre-implementation study in hospitals participating in a cluster randomised controlled trial. PLOS ONE . 2017;12(2):e0171932. doi:10.1371/journal.pone.0171932
• Pratt R, Xiong S, Kmiecik A, et al. The implementation of a smoking cessation and alcohol abstinence intervention for people experiencing homelessness. BMC Public Health . 2022;22:1260. doi:10.1186/s12889-022-13563-5
• Bossert J, Mahler C, Boltenhagen U, et al. Protocol for the process evaluation of a counselling intervention designed to educate cancer patients on complementary and integrative health care and promote interprofessional collaboration in this area (the CCC-Integrativ study). PLOS ONE . 2022;17(5):e0268091. doi:10.1371/journal.pone.0268091
• Lwin KS, Bhandari AKC, Nguyen PT, et al. Factors influencing implementation of health-promoting interventions at workplaces: protocol for a scoping review. PLOS ONE . 2022;17(10):e0275887. doi:10.1371/journal.pone.0275887
• Wilhelm AK, Schwedhelm M, Bigelow M, et al. Evaluation of a school-based participatory intervention to improve school environments using the Consolidated Framework for Implementation Research. BMC Public Health . 2021;21:1615. doi:10.1186/s12889-021-11644-5
• Timm L, Annerstedt KS, Ahlgren JÁ, et al. Application of the Theoretical Framework of Acceptability to assess a telephone-facilitated health coaching intervention for the prevention and management of type 2 diabetes. PLOS ONE . 2022;17(10):e0275576. doi:10.1371/journal.pone.0275576
• Laing L, Salema N-E, Jeffries M, et al. Understanding factors that could influence patient acceptability of the use of the PINCER intervention in primary care: a qualitative exploration using the Theoretical Framework of Acceptability. PLOS ONE . 2022;17(10):e0275633. doi:10.1371/journal.pone.0275633
• Renko E, Knittle K, Palsola M, Lintunen T, Hankonen N. Acceptability, reach and implementation of a training to enhance teachers’ skills in physical activity promotion. BMC Public Health . 2020;20:1568. doi:10.1186/s12889-020-09653-x
• Alexander SM, Agaba A, Campbell JI, et al. A qualitative study of the acceptability of remote electronic bednet use monitoring in Uganda. BMC Public Health . 2022;22:1010. doi:10.1186/s12889-022-13393
• May C, Rapley T, Mair FS, et al. Normalization Process Theory On-line Users’ Manual, Toolkit and NoMAD instrument. 2015. Accessed February 15, 2023. https://normalization-process-theory.northumbria.ac.uk/
• Davis S. Ready for prime time? Using Normalization Process Theory to evaluate implementation success of personal health records designed for decision making. Front Digit  Health . 2020;2:575951. doi:10.3389/fdgth.2020.575951
• Durand M-A, Lamouroux A, Redmond NM, et al. Impact of a health literacy intervention combining general practitioner training and a consumer facing intervention to improve colorectal cancer screening in underserved areas: protocol for a multicentric cluster randomized controlled trial. BMC Public Health . 2021;21:1684. doi:10.1186/s12889-021-11565
• Jones SE, Hamilton S, Bell R, Araújo-Soares V, White M. Acceptability of a cessation intervention for pregnant smokers: a qualitative study guided by Normalization Process Theory. BMC Public Health . 2020;20:1512. doi:10.1186/s12889-020-09608-2
• Ziegler E, Valaitis R, Yost J, Carter N, Risdon C. “Primary care is primary care”: use of Normalization Process Theory to explore the implementation of primary care services for transgender individuals in Ontario. PLOS ONE . 2019;14(4):e0215873. doi:10.1371/journal.pone.0215873
• Mackenzie M, Conway E, Hastings A, Munro M, O’Donnell C. Is ‘candidacy’ a useful concept for understanding journeys through public services? A critical interpretive literature synthesis. Soc Policy Adm . 2013;47(7):806-825. doi:10.1111/j.1467-9515.2012.00864.x
• Adeagbo O, Herbst C, Blandford A, et al. Exploring people’s candidacy for mobile health–supported HIV testing and care services in rural KwaZulu-Natal, South Africa: qualitative study. J Med Internet Res . 2019;21(11):e15681. doi:10.2196/15681
• Mackenzie M, Turner F, Platt S, et al. What is the ‘problem’ that outreach work seeks to address and how might it be tackled? Seeking theory in a primary health prevention programme. BMC Health Serv Res . 2011;11:350. doi:10.1186/1472-6963-11-350
• Liberati E, Richards N, Parker J, et al. Qualitative study of candidacy and access to secondary mental health services during the COVID-19 pandemic. Soc Sci Med. 2022;296:114711. doi:10.1016/j.socscimed.2022.114711
• Pearson O, Schwartzkopff K, Dawson A, et al. Aboriginal community controlled health organisations address health equity through action on the social determinants of health of Aboriginal and Torres Strait Islander peoples in Australia. BMC Public Health . 2020;20:1859. doi:10.1186/s12889-020-09943-4
• Freeman T, Baum F, Lawless A, et al. Revisiting the ability of Australian primary healthcare services to respond to health inequity. Aust J Prim  Health . 2016;22(4):332-338. doi:10.1071/PY14180
• Couzos S. Towards a National Primary Health Care Strategy: Fulfilling Aboriginal Peoples Aspirations to Close the Gap . National Aboriginal Community Controlled Health Organisation. 2009. Accessed February 15, 2023. https://researchonline.jcu.edu.au/35080/
• Napier AD, Ancarno C, Butler B, et al. Culture and health. Lancet . 2014;384(9954):1607-1639. doi:10.1016/S0140-6736(14)61603-2
• WHO. COVID-19 and the Social Determinants of Health and Health Equity: Evidence Brief . 2021. Accessed February 15, 2023. https://www.who.int/publications/i/item/9789240038387
• WHO. Social Determinants of Health . 2023. Accessed February 15, 2023. https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1
• McCrae JS, Robinson JAL, Spain AK, Byers K, Axelrod JL. The Mitigating Toxic Stress study design: approaches to developmental evaluation of pediatric health care innovations addressing social determinants of health and toxic stress. BMC Health Serv Res . 2021;21:71. doi:10.1186/s12913-021-06057-4
• Hosseinpoor AR, Stewart Williams J, Jann B, et al. Social determinants of sex differences in disability among older adults: a multi-country decomposition analysis using the World Health Survey. Int J  Equity   Health . 2012;11:52. doi:10.1186/1475-9276-11-52
• Kabore A, Afriyie-Gyawu E, Awua J, et al. Social ecological factors affecting substance abuse in Ghana (West Africa) using photovoice. Pan Afr Med J . 2019;34:214. doi:10.11604/pamj.2019.34.214.12851
• Bíró É, Vincze F, Mátyás G, Kósa K. Recursive path model for health literacy: the effect of social support and geographical residence. Front Public Health . 2021;9. doi:10.3389/fpubh.2021.724995
• Yuan B, Zhang T, Li J. Family support and transport cost: understanding health service among older people from the perspective of social-ecological model. Arch Public Health . 2022;80:173. doi:10.1186/s13690-022-00923-1
• Mahmoodi Z, Karimlou M, Sajjadi H, Dejman M, Vameghi M, Dolatian M. A communicative model of mothers’ lifestyles during pregnancy with low birth weight based on social determinants of health: a path analysis. Oman Med J . 2017 ;32(4):306-314. doi:10.5001/omj.2017.59
• Vella SA, Schweickle MJ, Sutcliffe J, Liddelow C, Swann C. A systems theory of mental health in recreational sport. Int J Environ Res Public Health . 2022;19(21):14244. doi:10.3390/ijerph192114244
• Henning S. The wellness of airline cabin attendants: A systems theory perspective. African Journal of Hospitality, Tourism and Leisure . 2015;4(1):1-11. Accessed February 15, 2023. http://www.ajhtl.com/archive.html
• Sutphin ST, McDonough S, Schrenkel A. The role of formal theory in social work research: formalizing family systems theory. Adv Soc Work . 2013;14(2):501-517. doi:10.18060/7942
• Colla R, Williams P, Oades LG, Camacho-Morles J. “A new hope” for positive psychology: a dynamic systems reconceptualization of hope theory. Front Psychol .  2022;13. doi:10.3389/fpsyg.2022.809053
• Engel GL. The need for a new medical model: a challenge for biomedicine. Science. 1977;196(4286):129–136. doi:10.1126/science.847460
• Wade DT, HalliganPW. The biopsychosocial model of illness: a model whose time has come. Clin Rehabi l. 2017;31(8):995–1004. doi:10.1177/0269215517709890
• Ip L, Smith A, Papachristou I, Tolani E. 3 Dimensions for Long Term Conditions – creating a sustainable bio-psycho-social approach to healthcare.  J Integr Care . 2019;19(4):5. doi:10.5334/ijic.s3005
• FrameWorks Institute. A Matter of Life and Death: Explaining the Wider Determinants of Health in the UK . FrameWorks Institute; 2022. Accessed February 15, 2023. https://www.frameworksinstitute.org/wp-content/uploads/2022/03/FWI-30-uk-health-brief-v3a.pdf
• Zemed A, Nigussie Chala K, Azeze Eriku G, Yalew Aschalew A. Health-related quality of life and associated factors among patients with stroke at tertiary level hospitals in Ethiopia. PLOS ONE . 2021;16(3):e0248481. doi:10.1371/journal.pone.0248481
• Finch E, Foster M, Cruwys T, et al. Meeting unmet needs following minor stroke: the SUN randomised controlled trial protocol. BMC Health Serv Res . 2019;19:894. doi:10.1186/s12913-019-4746-1

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

Share This Book

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Development of Conceptual Models to Guide Public Health Research, Practice, and Policy: Synthesizing Traditional and Contemporary Paradigms

Sonya s. brady.

Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, 55454, USA

Linda Brubaker

Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, 92037, USA

Cynthia S. Fok

Department of Urology, University of Minnesota Medical School, Minneapolis, MN, 55454, USA

Sheila Gahagan

Division of Academic General Pediatrics, University of California San Diego, San Diego, CA, 92093, USA

Cora E. Lewis

Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

Jessica Lewis

Yale School of Public Health, New Haven, CT, 06520, USA

Jerry L. Lowder

Division of Female Pelvic Medicine and Reconstructive Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA

Jesse Nodora

Department of Family Medicine and Public Health and Moores UC San Diego Cancer Center, University of California San Diego, La Jolla, CA, 92161, USA

Ann Stapleton

Department of Medicine, University of Washington, Seattle, WA, 98195, USA

Mary H. Palmer

School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

This applied paper is intended to serve as a “how to” guide for public health researchers, practitioners, and policy makers who are interested in building conceptual models to convey their ideas to diverse audiences. Conceptual models can provide a visual representation of specific research questions. They also can show key components of programs, practices, and policies designed to promote health. Conceptual models may provide improved guidance for prevention and intervention efforts if they are based on frameworks that integrate social ecological and biological influences on health and incorporate health equity and social justice principles. To enhance understanding and utilization of this guide, we provide examples of conceptual models developed by the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium. PLUS is a transdisciplinary U.S. scientific network established by the National Institutes of Health in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Research Consortium is developing conceptual models to guide its prevention research agenda. Research findings may in turn influence future public health practices and policies. This guide can assist others in framing diverse public health and prevention science issues in innovative, potentially transformative ways.

Public health and prevention science students, researchers, practitioners, and policy makers all stand to benefit by becoming skilled in the development of conceptual models. Over 25 years ago, Jo Anne Earp and Susan Ennett (1991) described how a conceptual model could be used to depict the mechanisms by which a selected set of risk and protective factors may be associated with a health behavior or outcome of interest, as well as the conditions under which such associations are typically observed. This work demonstrated how conceptual models can be used to provide a visual representation of specific research questions and display the key components of prevention and intervention programs, practices, and policies designed to promote health. Since Earp and Ennett’s contribution, many publications that can be used to generate conceptual models have been introduced to the public health sphere. These writings describe frameworks that integrate social ecological and biological influences on health and highlight the potential for health equity and social justice principles to guide public health research, practice, and policy. By integrating diverse perspectives, those who design conceptual models can consider a wide range of factors that may influence health. A better understanding of what influences health can lead to the development of more effective health promotion programs, practices, and policies, as well as more efficient use of limited public health resources. Conceptual model development is an increasingly valued skill. For example, the National Institutes of Health have called for the inclusion of conceptual models when teams of researchers and practitioners respond to specific requests for proposals to conduct research on health promotion, including mental health (RFA-MH-18-705), bladder health (RFA-DK-19-015), and shared decision-making between patients and providers (PA-16-424; NIH, n.d. ).

This paper is intended to serve as a contemporary guide for building conceptual models. It is consistent with the mission of Health Promotion Practice to publish practical tools that advance the science and art of health promotion and disease prevention, particularly with respect to achieving health equity, addressing social determinants of health, and advancing evidence-based health promotion practice. To enhance understanding, examples of conceptual model development are provided from the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium, a transdisciplinary scientific network established by the National Institute of Diabetes and Digestive and Kidney Diseases in 2015 to study bladder health and prevention of lower urinary tract symptoms (LUTS) in girls and women ( Harlow et al., 2018 ). LUTS encompass a variety of bothersome bladder symptoms, including urgency urinary incontinence (i.e., strong urge “to go” with urine loss before reaching a toilet), stress urinary incontinence (i.e., urine loss with physical activity or increases in abdominal pressure such as a cough or sneeze), bothersome frequent and/or urgent urination, nocturnal enuresis (i.e., bed-wetting), difficulty urinating, dribbling after urination, and bladder or urethral pain before, during, or after urination ( Abrams et al., 2010 ; Haylen et al., 2010). LUTS are common. For example, more than 200 million people worldwide and over 15% of women aged 40 years or older experience urinary incontinence, one of the most prevalent LUTS ( Minassian, Bazi, & Stewart, 2017 ; Norton & Brubaker, 2006 ).

While many multidisciplinary research networks focus on clinical treatment of LUTS, the PLUS Consortium stands alone in its focus on bladder health promotion and prevention of LUTS. Consistent with the World Health Organization’s (WHO) definition of health (WHO, 2006), the PLUS Consortium conceptualizes bladder health as “a complete state of physical, mental, and social well-being related to bladder function, and not merely the absence of LUTS,” with function that “permits daily activities, adapts to short term physical or environmental stressors, and allows optimal well-being (e.g., travel; exercise; social, occupational, or other activities)” ( Lukacz et al., 2018 ).

Conceptual models are different from other tools and concepts.

Table 1 highlights the distinction between conceptual models and closely related visual tools and concepts. The contrast between conceptual frameworks and conceptual models is of particular relevance to the present guide. A research-oriented conceptual framework encapsulates what is possible to study and is intentionally comprehensive; in contrast, a research-oriented conceptual model encapsulates what a team has prioritized and chosen to study and is intentionally focused in scope ( Earp & Ennett, 1991 ; Brady et al., 2018 ). Similarly, conceptual frameworks and models may depict the “universe” and selected focus, respectively, of public health practices and policies. The contrast between a theory and conceptual model is also of particular relevance to the present guide. While both theories and conceptual models describe associations among constructs in order to explain or predict outcomes, a theory is intentionally broad with respect to application. It can guide the development of one or more conceptual models to address a specific public health behavior or outcome. While a review of prominent theories is beyond the scope of this paper, several public health textbooks provide an overview of theories that may be used to guide etiologic research and health promotion programs, practices, and policies (e.g., DiClemente, Salazar, & Crosby, 2019 ; Edberg, 2015 ; Glanz, Rimer, & Viswanath, 2015 ; Simons-Morton, McLeroy, & Wedndel, 2012 ).

Distinctions between conceptual models and other visual tools and concepts used in public health and related disciplines.

Traditional and contemporary conceptualizations of public health can identify a broad range of factors that may function as determinants of health.

Traditional conceptual frameworks include social ecological and biopsychosocial models. Social ecological models , a foundation of public health approaches for more than 40 years ( McLeroy, Bibeau, Steckler, & Glanz, 1988 ; Sallis & Owen, 2015 ; Richard, Gauvin, & Raine, 2011 ), situate individuals within an ecosystem of risk and protective factors that extend outward from the intrapersonal level (e.g., biology, psychology) through the interpersonal (e.g., family, peers, partner), institutional (e.g., school, workplace, health clinic), community (e.g., cultural norms), and societal (e.g., policies, laws, economics) levels. These nested spheres of influence interact to produce individual and population health. Similarly, the biopsychosocial model posits that health is defined by a complex reciprocal interaction of biological, psychological, and social factors ( Engel, 1981 ). Given the focus of this paper, we note that both social ecological and biopsychosocial models are more consistent with the definition of a conceptual framework than a conceptual model (see Table 1 ).

Contemporary conceptualizations of public health enhance traditional frameworks by more explicitly integrating biology and social ecology, adopting life course perspectives, and incorporating health equity, social justice, and community engagement principles to guide research, practice, and policy. The Society-Behavior-Biology Nexus depicts nested spheres of influences both within and outside of an individual, who moves through life stages from infancy to old age ( Glass & McAtee, 2006 ). Systems of biological organization include multi-organ systems, cellular and molecular influences, and the genomic substrate. Levels of ecology include the micro (e.g., family, social networks), mezzo (e.g., schools, worksites, communities, healthcare systems), macro (e.g., states, nations), and global (e.g., geopolitics, environment). Biology and social ecology are integrated through the multi-level concept of embodiment (e.g., gene-environment interactions; impact of varying social-ecological resources on biology within and across populations) ( Glass & McAtee, 2006 ; Krieger, 2005 ). Social determinants are framed as societal constraints against and opportunities for health – risk regulators – which include material conditions; discriminatory practices, policies, and attitudes; neighborhood and community conditions; behavioral norms, rules, and expectations; conditions of work; and laws, policies, and regulations. Risk regulators can impact behavior or become embodied with respect to biological function ( Glass & McAtee, 2006 ; Krieger, 2005 ).

The WHO Conceptual Framework for Action on Social Determinants of Health describes how the structure of societies (i.e., governance, policies, values) determines population health ( Solar & Irwin, 2010 ). Social stratification by race, ethnicity, sex, gender, social class, and other factors leads to social hierarchies, which in turn shape social determinants of health. Distal structural determinants of health inequities (e.g., public policy, macroeconomics) are distinguished from more proximal social determinants of health (e.g., living and working conditions). The WHO framework asserts that societies produce health and disease, obligating policy makers to promote health equity and redress structural factors that produce under-resourced communities. Without such attention, health inequities evolve, often widening over time and across generations. The WHO framework can inform conceptual model development by encouraging the consideration of determinants at distal, structural levels (e.g., national policies).

Research teams have utilized contemporary conceptualizations of public health to promote health equity and social justice ( Warnecke et al., 2008 ; Balazs & Ray, 2014 ). For example, the National Institutes of Health (NIH) sponsored Centers for Population Health and Health Disparities developed a framework to show how distal factors (population-level policies and social conditions, institutional contexts) influence intermediate social context (e.g., collective efficacy, social capital), social relationships (e.g., networks, support, and influence), and physical context (e.g., building quality, neighborhood stability), which in turn influence factors that are more proximal to health (individual demographics and risk behaviors, biologic responses and pathways) ( Warnecke et al., 2008 ). The Energy and Resources Group at the University of California, Berkeley developed a framework to display mechanisms through which natural, built, and sociopolitical factors, along with state, county, and community actors, can create drinking water disparities ( Balazs & Ray, 2014 ). These frameworks highlight the key role of distal structural factors in both generating health inequities and remedying them.

Community partners can aid in developing conceptual models.

Increasingly, teams are incorporating community-engaged approaches in the development of research, practice, and policy (e.g., community members actively contributing to problem definition, agenda setting, implementation, and dissemination) ( Warnecke et al., 2008 ; O’Mara-Eves et al., 2013 ). Different resources exist to guide community engagement and enhance the likelihood of sustained, relevant action. For example, Lezine and Reed (2007) outlined different steps to build and apply political will in the development and implementation of public health policy; their approach integrates scientific evidence and community participation. Cacari-Stone and colleagues (2014) developed a conceptual model to show how community-based participatory research (CBPR), one approach to community engagement, can lead to policy change.

Three Steps of Conceptual Model Development.

The development of conceptual models can be divided into three basic steps: (1) identify resources for idea generation; (2) consider risk and protective factors; and (3) select factors for inclusion in the conceptual model. First, team members identify existing conceptual frameworks and models, theories, and key stakeholders (e.g., practitioners, policy makers, community members) that will serve as resources for idea generation. This step defines the “universe” of factors that can be studied in relation to specific health behaviors or outcomes of interest. Second, team members systematically consider risk and protective factors suggested by resources. This step highlights the importance of carefully selecting resources for idea generation; the risk and protective factors considered by a team will be constrained by its selected frameworks and models, theories, and stakeholders. Existing evidence linking risk and protective factors to the health behaviors or outcomes under study, as well as potential effect modifiers and confounders, can be identified through literature reviews. When data are insufficient, a team may wish to conduct key stakeholder interviews, focus groups, and other forms of hypothesis-generating data collection. The third step in the development of conceptual models is to narrow down considered risk and protective factors to those that will be included in the conceptual model. This can be achieved through a combination of theoretically-based, key stakeholder-based, and evidence-based rationales. Theories point to clusters of risk and protective factors that could be studied in relation to health behaviors or outcomes of interest, or targeted through prevention or intervention efforts. Key stakeholders can assess the relevance of different theories to a given public health context and suggest additional risk and protective factors that seem critical to the context. Findings from the extant literature can provide evidence in support of different links in the conceptual model.

If the intent of building a conceptual model is to develop an evidence-based program, practice, or policy, a team can conduct a literature review to answer the following “narrowing down” questions: (a) Is the risk or protective factor strongly linked to the health behavior or outcome of interest? (b) Have previous prevention or intervention programs, practices, or policies shown that the risk or protective factor is feasible to modify? (c) Was health improved as a result of modifying the risk or protective factor? Risk and protective factors can be retained in the conceptual model if they are strongly supported by evidence and judged highly relevant to context.

When the intent of building a conceptual model is to conduct research to better understand a health behavior or outcome, a team may choose to consult existing theories, key stakeholders, and the evidence-base for guidance in selecting risk and protective factors. To maximize potential public health impact, a team can answer the following “narrowing down” question: What potential risk and protective factors are judged to be highly likely to influence health behaviors or outcomes of interest? Ideally, the answers to public health research questions will expand the evidence base in a way that can directly inform programs, practices, and policies. Expansion of the evidence-base can be accomplished in a variety of potentially transformative ways, including the synthesis of ideas from more than one discipline and the application of paradigms from one discipline to another.

Regardless of the approach and rationale used to select risk and protective factors, the utility of the conceptual model may be enhanced by answering the final three sets of questions: (a) Have key “mechanistic factors” been considered and included in the model? What biological, psychological, and social processes might explain links between identified risk and protective factors and health behaviors or outcomes of interest? (b) Have key “upstream factors” been considered and included in the model? For example, are there societal and institutional policies and practices that serve as facilitators or barriers to health? (c) Have key “effect modifiers” been considered and included in the model? For example, are there factors that might make prevention or intervention programs, practices, or policies more or less effective among specific communities and populations?

Examples from the PLUS Research Consortium.

The PLUS Consortium is comprised of a transdisciplinary network of professionals, including community advocates, health care professionals, and scientists specializing in pediatrics, adolescent medicine, gerontology and geriatrics, nursing, midwifery, behavioral medicine, preventive medicine, psychiatry, neuroendocrinology, reproductive medicine, female pelvic medicine and reconstructive surgery, urology, infectious diseases, clinical and social epidemiology, prevention science, medical sociology, psychology, women’s studies, sexual and gender minority health, community-engaged research, community health promotion, scale development, research methods, and biostatistics. The PLUS Consortium has developed several conceptual models to guide research questions that will test whether specific risk and protective factors contribute to LUTS and bladder health.

Because the evidence-base for LUTS prevention is sparse, the traditional and contemporary conceptualizations of public health reviewed above, as well as expertise of PLUS investigators, were used as key resources to identify potential risk and protective factors for study (Step 1). Traditional and contemporary conceptualizations of public health encouraged consortium members to step outside of their disciplinary “comfort zones” to integrate social ecological and biological influences on health across the life course and consider the potential for health equity and social justice principles to guide the consortium’s prevention research agenda. While all of the conceptualizations reviewed above were considered, Glass and McAtee’s Society-Behavior-Biology Nexus was particularly influential because it visually represented different levels of social ecology and biology across the life course, as well as the process of embodiment. PLUS members served as an initial key stakeholder group that generated a conceptual framework and over 400 risk and protective factors prioritized for study in relation to bladder health and LUTS (Step 2) ( Brady et al., 2018 ). The conceptual models presented in this paper represent the work of subsets of consortium members who designed models to guide specific research questions (Step 3). Models were designed with the assistance of public health and prevention science team members who were familiar with social ecological frameworks and the development of conceptual models. Initial development of models occurred in real time during in-person and virtual (WebEx) meetings. This was often followed by revision of models via emailed chains of conversation. One person with experience in conceptual model development was responsible for integrating and communicating comments and mutual decisions, as well as revising the models.

Each conceptual model featured in this paper represents hypothesized associations between constructs; some links in each model are supported by existing evidence, while others are based on theoretical or biological plausibility. Figure 1 highlights institutional-level factors in relation to bladder health and LUTS, while Figure 2 highlights family- and community-level factors and Figure 3 highlights societal and commercial factors.

Work-related structural and social influences on musculoskeletal function and bladder health: Hypothesized mechanisms.

Explanation of Pathways: Four different work-related factors (shaded boxes) affect different aspects of musculoskeletal function, which in turn affect bladder health and LUTS. Workplace physical and psychological demands directly affect musculoskeletal function. Workplace ergonomics and travel/commute patterns indirectly affect musculoskeletal function through prolonged sitting or standing and posture (mediation pathways).

Trajectories of risk and resilience among individuals and communities exposed to ACEs and traumatic stressors: Hypothesized mechanisms.

Explanation of Pathways: Executive functioning difficulties and central nervous system dysregulation are shown in a single, partitioned box because these constructs are hypothesized to covary in their manifestation. Direct effects between two adjacent constructs are shown by solid lines (1a, 2a, 3a, 4, 5); effect modification by resources for resilience (shaded box) is shown by dashed lines (1b, 2b, 3b). ADHD: Attention-Deficit/Hyperactivity Disorder.

Societal and commercial influences on bladder health and LUTS: Hypothesized mechanisms involving fast food and soda.

Explanation of Pathways: This conceptual model highlights hypothesized mechanisms (mediators) that can explain associations between societal and commercial factors (shaded boxes) and bladder health and LUTS. This model can guide a set of statistical analyses that require the identification of predictor, mediating, and outcome variables. The model does not reflect the full complexity of associations that likely exist among constructs (e.g., bi-directional associations, feedback loops; see Systems Model entry in Table 1 ).

Figure 1 depicts a basic conceptual model showing how specific work-related structural and social factors may influence musculoskeletal function, which in turn may impact bladder health and LUTS development. Four key aspects of musculoskeletal dysfunction are overuse injury, strain, pain, and weakness (see center-right of Figure 1 ), which may be directly and indirectly influenced by work-related factors. The top, bottom, and left-most boxes depict work-related factors that are external to the individual and arguably imposed by society and institutions. Workplace physical and psychological demands are shown to directly impact musculoskeletal function. Workplace physical demands (e.g., repetitive heavy lifting) may result in musculoskeletal dysfunction, which in turn may lead to LUTS ( Park & Palmer, 2015 ). In addition, workplace psychological demands (e.g., job performance pressures, conflict with coworkers, inequitable expectations and evaluations of work) may be accompanied by stress, anxiety, and other forms of negative affect ( Larsman, Kadefors, & Sandsjö, 2013 ), which may lead to chronically increased pelvic floor muscle dysfunction and LUTS ( van der Velde, Laan, & Everaerd, 2001 ). Workplace ergonomics (e.g., improper chair or desk height) and travel/commute patterns (e.g., daily, long commutes and long airplane flights) may indirectly impact musculoskeletal dysfunction through prolonged sitting or standing and poor posture ( Barone Gibbs et al., 2018 ).

Additional research is needed to support hypothesized associations in Figure 1 , which are based in large part on the authors’ clinical and community-based observations. If different links are supported, corresponding workplace policies and practices can be promoted to ensure that physical demands are offset by varying the type and intensity of activity and providing breaks; psychological demands are fair, reasonable, and offset by supports; and workplace ergonomics are conducive to the health of all employees, regardless of status within the organization. In addition, local and state governments can support policies and practices that ensure adequate access to acceptable bathroom facilities along transportation routes and when possible, within public transportation conveyances.

Figure 2 shows an example of a more complex conceptual model. A trajectory of risk among individuals or communities exposed to adverse childhood experiences (ACEs) (e.g., abuse, neglect, household disruptions) (Felitti et al., 1998) and other traumatic stressors can be seen by following the solid lines from left to right. ACEs and traumatic stressors indirectly affect local dysregulation through two potential pathways: (I) development of executive functioning difficulties and central nervous system dysregulation (shown by 1a links) ( Nusslock & Miller, 2016 ; Smith et al., 2016 ), which in turn lead to local dysregulation (shown by link 4) ( Kanter et al., 2016 ); and (II) development of depression, anxiety, and ADHD symptoms (shown by 2a links), which in turn lead to executive functioning difficulties and central nervous system dysregulation (shown by link 3a) ( Nusslock & Miller, 2016 ), which then leads to local dysregulation (shown by link 4) ( Kanter et al., 2016 ; Yousefichaijan, Sharafkhah, Rafiei, & Salehi, 2016 ). Constructs that explain associations between stressful life circumstances and LUTS may collectively be thought of as a “chain of mediation,” in that they lie along a hypothesized causal, sequential pathway. Figure 2 also shows how a trajectory of risk/chain of mediation may be weakened or broken at different points along the pathway. The dashed lines of Figure 2 show modification of effects (“effect modification”) by resources for resilience (i.e., coping, social support). Effects of stressful life circumstances on LUTS are weakened in the presence of resources for resilience (shown by the dashed lines 1b, 2b, and 3b).

Although several of the links in Figure 2 are supported by evidence, additional research is needed. Figure 2 illustrates the importance of structural factors that stratify the citizens of a society into communities that are more or less likely to experience adverse childhood experiences and traumatic stressors, and have more or less opportunities to garner resources for resilience ( Glass & McAtee, 2006 ; Solar & Irwin, 2010 ; Warnecke et al., 2008 ). Policies attempting to ensure equitable allocation of resources, including but not limited to health care, are essential to preventing and weakening trajectories of risk that disproportionately impact under-resourced communities and families.

Figure 3 , our final example, highlights broader, societal and commercial influences on bladder health and LUTS, along with environmental, behavioral, and biological mechanisms specific to fast food and soda consumption. Consistent with the WHO Conceptual Framework for Action on Social Determinants of Health ( Solar & Irwin, 2010 ), Figure 3 begins with societal structures. Governance and policies shape the built environments of communities, in part through zoning of fast food restaurants, convenience stores, grocery stores, and farmers markets; these, in turn, impact the availability of fast food and soda in communities ( Sallis & Glanz, 2009 ). Additional policies can impact the affordability of fast food and soda relative to healthy products (e.g., taxation of sugar-sweetened beverages; subsidies for fresh produce) ( Franck, Grandi, & Eisenberg, 2013 ), as well as the advertising and marketing of fast food and beverages, especially towards children ( Harris et al., 2015 ). Low-income communities of color in the United States have historically received fewer resources as a result of inequitable policies; they have also been targeted by the fast food and soda industries ( Sallis & Glanz, 2009 ; Harris et al., 2015 ).

Availability, relative affordability, advertising, and marketing of fast food and soda within a community increase the likelihood that residents will consume “super-sized” food portions and soda, which contributes to obesity ( Sallis & Glanz, 2009 ; Harris et al., 2015 ). Obesity may directly impact LUTS by intra-abdominal pressure on the bladder ( Bavendam et al., 2016 ); it may also impact LUTS through diabetes-related mechanisms, including neurogenic bladder and urinary tract infections ( Bavendam et al., 2016 ; Podnar & Vodusek, 2015 ). Diet soda, which many individuals embrace as a means to reduce caloric intake and combat obesity, contains components that may increase urine volume (caffeine) and harm the health of the bladder lining (artificial sweeteners, carbonation/acidity) (Robinson, Hanna-Mitchell, Rantell, Thiagamoorthy, & Cardozo, 2015). A healthy bladder may be maintained or restored by healthy food and beverage choices; Figure 3 highlights constraints on healthy choices that are determined by upstream, societal factors.

Because the PLUS Research Consortium is just beginning its prevention research agenda, its current models are intended to guide etiologic research, as opposed to selection, implementation, and evaluation of health promotion and prevention strategies. Broader planning frameworks exist for this purpose, including PRECEDE-PROCEED and intervention mapping ( Bartholomew, Markham, Mullen, & Fernández, 2015 ; Bartholomew, Parcel, & Kok, 1998 ; Green & Kreuter, 2005 ), the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Strategic Prevention Framework (2017) , and the Center for Disease Control and Prevention’s (CDC) Framework for Program Evaluation in Public Health (1999) . These frameworks not only guide practitioners in assessing risk and protective factors at different levels of social ecology that may influence health, but also provide a structure for applying theories and conceptual models to the planning and evaluation of health promotion programs, practices, and policies. The PLUS Research Consortium will utilize existing planning frameworks when its work progresses to the point of designing, implementing, and evaluating bladder health promotion and LUTS prevention strategies through research.

Lessons Learned and Recommendations for Other Conceptual Model Development Teams.

After developing the conceptual models and supporting materials presented in this paper, authors reflected on lessons they had learned and what they would recommend to other teams.

Recommendation 1: Develop a shared language.

Students, researchers, practitioners, and policy makers interested in developing conceptual models may benefit from reviewing the terms in Table 1 , determining what is consistent with and distinct from their own discipline and training, and identifying additional tools and concepts that could aid in conceptual model development. Few of this paper’s authors were initially familiar with all of the visual tools and related concepts defined in Table 1 . Terms were added not only by authors, but also by other PLUS Consortium members (e.g., epidemiologists recommended the inclusion of “directed acyclic graph” and “systems model”). Teams who are developing conceptual models may develop a shared language through the process of reviewing, adding, and defining terms.

Recommendation 2: Establish a conceptual framework before developing a conceptual model.

Authors appreciated the distinction between conceptual frameworks and models, particularly with respect to how a framework could be a starting point to broaden one’s conceptualization of health beyond one’s own disciplinary training. Consortium members valued the integration of social ecological, behavioral, and biological perspectives of what influences health, as well as the opportunity to incorporate multiple levels of influence into a single conceptual model and corresponding set of research questions. Consortium members appreciated how the creation and refinement of conceptual models could then assist in clarifying specific research questions; identifying potential pathways through which different risk and protective factors may influence a health outcome; examining and challenging one’s own disciplinary assumptions; and articulating what is known or speculative with respect to the factors that influence health.

Recommendation 3: Seek to develop a diverse team and solicit input from others.

Authors appreciated how steps of conceptual model development included the consideration of how community partners and other key stakeholders can become involved in the process of development. By design, the PLUS Research Consortium includes community advocates, community-engaged researchers, and health care professionals and scientists representing a broad array of disciplines. Authors did not reach beyond the PLUS Consortium to develop the conceptual models featured in this paper, in part because the present paper was intended to describe the process of conceptual model development, rather than to present definitive models. Other conceptual model development teams may benefit from soliciting the input of individuals who are not well represented on their team, including community members, researchers, practitioners, and policy makers.

Recommendation 4: Anticipate and embrace the iterative, “trial and error” nature of conceptual model development.

Early in the process of developing conceptual models, authors developed a shared understanding that it was not necessary for all proposed links in a conceptual model to be informed by existing evidence. Theory, clinical observations, and the lived experience of community members are valid sources of information, as well. Authors also came to appreciate that it was not necessary to develop the “perfect” model during a first attempt to understand a health behavior or outcome, or to select the key components of an evidence-based program, practice, or policy. Indeed, attempting to achieve perfection may stifle creativity and innovation. The conceptual models presented in this paper were developed iteratively, both within the team of authors and consortium members who assisted in their development (see Acknowledgements ). Conceptual models should be evaluated through research, which may support or fail to support proposed links in a model. Conceptual models are meant to be refined, not only during their initial stage of development, but also in response to new information that is gleaned through subsequent research.

Summary and Conclusion.

Researchers, practitioners, and policy makers can use conceptual models to convey ideas to diverse audiences. We posit that conceptual models may have the greatest impact on public health if they integrate social ecological and biological influences on health and highlight the potential for health equity and social justice principles to guide public health research, practice, and policy. To illustrate this point, we have provided examples of conceptual model development from the P revention of L ower U rinary Tract S ymptoms (PLUS) Research Consortium, a transdisciplinary scientific network established in the United States in 2015 to promote bladder health and prevent lower urinary tract symptoms, an emerging public health and prevention priority. The PLUS Consortium is developing conceptual models to guide its bladder health promotion and LUTS prevention research agenda. In concert with other researchers and community partners, the PLUS Consortium will be poised to inform future public health practices and policies. We hope our shared work will assist others in framing diverse public health matters in innovative, potentially transformative ways.

Acknowledgements

The authors acknowledge special contributions to featured conceptual models by the following PLUS Research Consortium members: Amanda Berry, Neill Epperson, Colleen Fitzgerald, Missy Lavender, Ariana Smith, and Beverly Williams. The authors also acknowledge the foundational work of Jo Anne Earp, Professor Emerita, and Susan T. Ennett, Professor, Department of Health Behavior, Gillings School of Public Health, University of North Carolina, Chapel Hill. Dr. Earp and Dr. Ennett’s pioneering “how to” guide for building conceptual models, published in 1991, inspired the present guide. In addition, the authors acknowledge Kenneth L. McLeroy, Professor Emeritus and retired Regents and Distinguished Professor, School of Public Health, Texas A&M University, for helpful discussion about manuscript content.

Participating PLUS research centers at the time of this writing are as follows:

Loyola University Chicago - 2160 S. 1 st Avenue, Maywood, Il 60153-3328

Linda Brubaker, MD, MS, Multi-PI; Elizabeth Mueller, MD, MSME, Multi-PI; Colleen M. Fitzgerald, MD, MS, Investigator; Cecilia T. Hardacker, RN, MSN, Investigator; Jeni Hebert-Beirne, PhD, MPH, Investigator; Missy Lavender, MBA, Investigator; David A. Shoham, PhD, Investigator

University of Alabama at Birmingham - 1720 2nd Ave South, Birmingham, AL 35294

Kathryn Burgio, PhD, PI; Cora E. Lewis, MD, MSPH, Investigator; Alayne Markland, DO, MSc, Investigator; Gerald McGwin, PhD, Investigator; Beverly Williams, PhD, Investigator

University of California San Diego - 9500 Gilman Drive, La Jolla, CA 92093-0021

Emily S. Lukacz, MD, PI; Sheila Gahagan, MD, MPH, Investigator; D. Yvette LaCoursiere, MD, MPH, Investigator; Jesse N. Nodora, DrPH, Investigator

University of Michigan - 500 S. State Street, Ann Arbor, MI 48109

Janis M. Miller, PhD, MSN, PI; Lawrence Chin-I An, MD, Investigator; Lisa Kane Low, PhD, MS, CNM, Investigator

University of Pennsylvania – Urology, 3rd FL West, Perelman Bldg, 34th & Spruce St, Philadelphia, PA 19104

Diane Kaschak Newman, DNP, ANP-BC, FAAN PI; Amanda Berry, PhD, CRNP, Investigator; C. Neill Epperson, MD, Investigator; Kathryn H. Schmitz, PhD, MPH, FACSM, FTOS, Investigator; Ariana L. Smith, MD, Investigator; Ann Stapleton, MD, FIDSA, FACP, Investigator; Jean Wyman, PhD, RN, FAAN, Investigator

Washington University in St. Louis - One Brookings Drive, St. Louis, MO 63130

Siobhan Sutcliffe, PhD, PI; Colleen McNicholas, DO, MSc, Investigator; Aimee James, PhD, MPH, Investigator; Jerry Lowder, MD, MSc, Investigator;

Yale University - PO Box 208058 New Haven, CT 06520-8058

Leslie Rickey, MD, PI; Deepa Camenga, MD, MHS, Investigator; Shayna D. Cunningham, PhD, Investigator; Toby Chai, MD, Investigator; Jessica B. Lewis, PhD, MFT, Investigator

Steering Committee Chair: Mary H. Palmer, PhD, RN: University of North Carolina

NIH Program Office: National Institute of Diabetes and Digestive and Kidney Diseases, Division of Kidney, Urologic, and Hematologic Diseases, Bethesda, MD

NIH Project Scientist: Tamara Bavendam MD, MS; Project Officer: Ziya Kirkali, MD; Scientific Advisors: Chris Mullins, PhD and Jenna Norton, MPH; Scientific and Data Coordinating Center (SDCC): University of Minnesota - 3 Morrill Hall, 100 Church St. S.E., Minneapolis MN 55455

Bernard Harlow, PhD, Multi-PI; Kyle Rudser, PhD, Multi-PI; Sonya S. Brady, PhD, Investigator; John Connett, PhD, Investigator; Haitao Chu, MD, PhD, Investigator; Cynthia Fok, MD, MPH, Investigator; Todd Rockwood, PhD, Investigator; Melissa Constantine, PhD, MPAff, Investigator

This work of the Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium was supported by the National Institutes of Health (NIH) through cooperative agreements (grant numbers U01DK106786, U01DK106853, U01DK106858, U01DK106898, U01DK106893, U01DK106827, U01DK106908, U01DK106892). Additional support was provided by the National Institute on Aging, NIH Office of Research on Women’s Health, and NIH Office of Behavioral and Social Sciences Research. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

Contributor Information

Sonya S. Brady, Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, 55454, USA.

Linda Brubaker, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, 92037, USA.

Cynthia S. Fok, Department of Urology, University of Minnesota Medical School, Minneapolis, MN, 55454, USA.

Sheila Gahagan, Division of Academic General Pediatrics, University of California San Diego, San Diego, CA, 92093, USA.

Cora E. Lewis, Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.

Jessica Lewis, Yale School of Public Health, New Haven, CT, 06520, USA.

Jerry L. Lowder, Division of Female Pelvic Medicine and Reconstructive Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA.

Jesse Nodora, Department of Family Medicine and Public Health and Moores UC San Diego Cancer Center, University of California San Diego, La Jolla, CA, 92161, USA.

Ann Stapleton, Department of Medicine, University of Washington, Seattle, WA, 98195, USA.

Mary H. Palmer, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

• Abrams P, Andersson KE, Birder L, Brubaker L, Cardozo L, Chapple C, … & Wyndaele JJ (2010). Fourth International Consultation on Incontinence Recommendations of the International Scientific Committee: Evaluation and treatment of urinary incontinence, pelvic organ prolapse, and fecal incontinence . Neurourology and Urodynamics: Official Journal of the International Continence Society , 29 ( 1 ), 213–240. [ PubMed ] [ Google Scholar ]
• Balazs CL, & Ray I (2014). The drinking water disparities framework: On the origins and persistence of inequities in exposure . American Journal of Public Health , 104 ( 4 ), 603–611. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Baron RM, & Kenny DA (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations . Journal of Personality and Social Psychology , 51 ( 6 ), 1173. [ PubMed ] [ Google Scholar ]
• Bartholomew LK, ., Markham C, Mullen P, & Fernández ME. (2015). Planning models for theory-based health promotion interventions In Glanz K, Rimer BK, & Viswanath K. (Eds.) Health Behavior: Theory, Research, and Practice (5th ed., pp. 359–387). San Francisco, CA: Jossey Bass. [ Google Scholar ]
• Bartholomew LK, Parcel GS, & Kok G (1998). Intervention mapping: A process for developing theory- and evidence-based health education programs . Health Education & Behavior , 25 ( 5 ), 545–563. [ PubMed ] [ Google Scholar ]
• Bavendam TG, Norton JM, Kirkali Z, Mullins C, Kusek JW, Star RA, & Rodgers GP (2016). Advancing a comprehensive approach to the study of lower urinary tract symptoms . The Journal of Urology, 196 ( 5 ), 1342–1349. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brady SS, Bavendam TG, Berry A, Fok CS, Gahagan S, Goode PS, … & Lukacz ES. (2018). The Prevention of Lower Urinary Tract Symptoms (PLUS) in girls and women: Developing a conceptual framework for a prevention research agenda . Neurourology and Urodynamics , 37 ( 8 ), 2951–2964. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cacari-Stone L, Wallerstein N, Garcia AP, & Minkler M (2014). The promise of community-based participatory research for health equity: a conceptual model for bridging evidence with policy . American Journal of Public Health , 104 ( 9 ), 1615–1623. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Centers for Disease Control and Prevention. (2004). Evaluation guide: Developing and using a logic model . Atlanta, GA: Division of Heart Disease and Stroke Prevention. [ Google Scholar ]
• Centers for Disease Control and Prevention. (1999). Framework for program evaluation in public health . MMWR , 48 (No. RR-11 ). Retrieved June 2, 2019 from https://www.cdc.gov/eval/framework/index.htm [ Google Scholar ]
• Coie JD, Watt NF, West SG, Hawkins JD, Asarnow JR, Markman HJ, … & Long B (1993). The science of prevention: A conceptual framework and some directions for a national research program . American Psychologist , 48 ( 10 ), 1013. [ PubMed ] [ Google Scholar ]
• Definition of conceptual framework. Mosby’s Medical Dictionary , 8th edition The Free Dictionary by Farlex; Retrieved January 31, 2018 from https://medical-dictionary.thefreedictionary.com/conceptual+framework Accessed [ Google Scholar ]
• DiClemente RJ, Salazar LF, & Crosby RA (2019). Health Behavior Theory for Public Health: Principles, Foundations, and Applications (2nd ed.). Burlington, MA: Jones & Bartlett Learning. [ Google Scholar ]
• Earp JA, & Ennett ST (1991). Conceptual models for health education research and practice . Health Education Research , 6 ( 2 ), 163–171. [ PubMed ] [ Google Scholar ]
• Edberg M (2015). Essentials of Health Behavior: Social and Behavioral Theory in Public Health (2nd ed.). In Riegelman R (Series Ed.), Essential Public Health . Burlington, MA: Jones & Bartlett Learning. [ Google Scholar ]
• Engel GL (1981, January). The clinical application of the biopsychosocial model . In The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine , 6 ( 2 ), 101–124. [ PubMed ] [ Google Scholar ]
• Fairchild AJ, & MacKinnon DP (2009). A general model for testing mediation and moderation effects . Prevention Science , 10 ( 2 ), 87–99. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Franck C, Grandi SM, & Eisenberg MJ (2013). Taxing junk food to counter obesity . American Journal of Public Health , 103 ( 11 ), 1949–1953. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gibbs BB, Hergenroeder AL, Perdomo SJ, Kowalsky RJ, Delitto A, & Jakicic JM (2018). Reducing sedentary behaviour to decrease chronic low back pain: the stand back randomised trial . Occup Environ Med , 75 ( 5 ), 321–327. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Glanz K, Rimer BK, & Viswanath K (Eds.). (2015). Health Behavior: Theory, Research, and Practice (5th ed.). San Francisco, CA: Jossey Bass. [ Google Scholar ]
• Glanz K, Rimer BK, & Viswanath K (2015). Theory, research, and practice in health behavior In: Glanz K, Rimer BK, & Viswanath K. (Eds.). Health Behavior: Theory, Research, and Practice . 5th ed. (pp. 23–41). San Francisco, CA: Jossey Bass. [ Google Scholar ]
• Glass TA, & McAtee MJ (2006). Behavioral science at the crossroads in public health: extending horizons, envisioning the future . Social Science & Medicine , 62 ( 7 ), 1650–1671. [ PubMed ] [ Google Scholar ]
• Glymour MM (2006). Using causal diagrams to understand common problems in social epidemiology In Oakes JM & Kaufman JS (Eds.), Methods in social epidemiology (1st ed., pp. 393–428). San Francisco, CA: Jossey-Bass. [ Google Scholar ]
• Green LW. , & Kreuter MW. (2005). Health program planning: An educational and ecological approach (4th ed.). New York: McGraw-Hill. [ Google Scholar ]
• Greenland S, Pearl J, & Robins JM (1999). Causal diagrams for epidemiologic research . Epidemiology , 37–48. [ PubMed ] [ Google Scholar ]
• Halen BT, de Ridder D, Freeman R, Swift S, Berghmans B, & Lee J (2010). An International Urogynecology Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction . Neurourol Urodyn , 29 , 4–20. [ PubMed ] [ Google Scholar ]
• Harlow BL, Bavendam TG, Palmer MH, Brubaker L, Burgio KL, Lukacz ES, … & Simons-Morton D. (2018). The prevention of lower urinary tract symptoms (PLUS) research consortium: A transdisciplinary approach toward promoting bladder health and preventing lower urinary tract symptoms in women across the life course . Journal of Women’s Health , 27 ( 3 ), 283–289. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Harris JL, Shehan C, Gross R, Kumanyika S, Lassiter V, Ramirez AG, & Gallion K (2015). Food advertising targeted to Hispanic and Black youth: Contributing to health disparities . Rudd Center for Food Policy & Obesity. [ Google Scholar ]
• Joffe M, & Mindell J (2006). Complex causal process diagrams for analyzing the health impacts of policy interventions . American Journal of Public Health , 96 ( 3 ), 473–479. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Johnston LM, Matteson CL, & Finegood DT (2014). Systems science and obesity policy: a novel framework for analyzing and rethinking population-level planning . American Journal of Public Health , 104 ( 7 ), 1270–1278. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kanter G, Komesu YM, Qaedan F, Jeppson PC, Dunivan GC, Cichowski SB, & Rogers RG (2016). Mindfulness-based stress reduction as a novel treatment for interstitial cystitis/bladder pain syndrome: a randomized controlled trial . International Urogynecology Journal , 27 ( 11 ), 1705–1711. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kia-Keating M, Dowdy E, Morgan ML, & Noam GG (2011). Protecting and promoting: An integrative conceptual model for healthy development of adolescents . Journal of Adolescent Health , 48 ( 3 ), 220–228. [ PubMed ] [ Google Scholar ]
• Krieger N (2005). Embodiment: a conceptual glossary for epidemiology . Journal of Epidemiology & Community Health , 59 ( 5 ), 350–355. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Larsman P, Kadefors R, & Sandsjö L (2013). Psychosocial work conditions, perceived stress, perceived muscular tension, and neck/shoulder symptoms among medical secretaries . International Archives of Occupational and Environmental Health , 86 ( 1 ), 57–63. [ PubMed ] [ Google Scholar ]
• Leischow SJ, & Milstein B (2006). Systems thinking and modeling for public health practice . American Journal of Public Health , 96 ( 3 ) 403–405. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lezine DA, & Reed GA (2007). Political will: a bridge between public health knowledge and action . American Journal of Public Health , 97 ( 11 ), 2010–2013. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lukacz ES, Bavendam TG, Berry A, Fok CS, Gahagan S, Goode PS, … & Brady SS. (2018). Defining bladder health in women and girls: Implications for research, clinical practice, and public health promotion . Journal of Women’s Health , 27 ( 8 ) 974–981. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• McLeroy KR, Bibeau D, Steckler A, & Glanz K (1988). An ecological perspective on health promotion programs . Health Education Quarterly , 15 ( 4 ), 351–377. [ PubMed ] [ Google Scholar ]
• Minassian VA, Bazi T, & Stewart WF (2017). Clinical epidemiological insights into urinary incontinence . International Urogynecology Journal , 28 ( 5 ), 687–696. [ PubMed ] [ Google Scholar ]
• National Institutes of Health (n.d.). Find Funding: NIH Guide for Grants and Contracts . Retrieved September 10, 2019 from https://grants.nih.gov/funding/searchguide/index.html#/
• Norton P, & Brubaker L (2006). Urinary incontinence in women . The Lancet , 367 ( 9504 ), 57–67. [ PubMed ] [ Google Scholar ]
• Nusslock R, & Miller GE (2016). Early-life adversity and physical and emotional health across the lifespan: A neuroimmune network hypothesis . Biological Psychiatry , 80 ( 1 ), 23–32. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• O’Mara-Eves A, Brunton G, McDaid G, Oliver S, Kavanagh J, Jamal F, … & Thomas J. (2013). Community engagement to reduce inequalities in health: a systematic review, meta-analysis and economic analysis . Public Health Research , 1 ( 4 ). [ PubMed ] [ Google Scholar ]
• Park J, & Palmer MH (2015). Factors associated with incomplete bladder emptying in older women with overactive bladder symptoms . Journal of the American Geriatrics Society , 63 ( 7 ), 1426–1431. [ PubMed ] [ Google Scholar ]
• Podnar S, & Vodušek DB (2015). Lower urinary tract dysfunction in patients with peripheral nervous system lesions . Handbook of Clinical Neurology , 130 , 203. [ PubMed ] [ Google Scholar ]
• Richard L, Gauvin L, & Raine K (2011). Ecological models revisited: their uses and evolution in health promotion over two decades . Annual Review of Public Health , 32 , 307–326. [ PubMed ] [ Google Scholar ]
• Robinson D, Hanna-Mitchell A, Rantell A, Thiagamoorthy G, & Cardozo L (2017). Are we justified in suggesting change to caffeine, alcohol, and carbonated drink intake in lower urinary tract disease? Report from the ICI-RS 2015 . Neurourology and Urodynamics , 36 ( 4 ), 876–881. [ PubMed ] [ Google Scholar ]
• Sallis JF, & Glanz K (2009). Physical activity and food environments: solutions to the obesity epidemic . The Milbank Quarterly , 87 ( 1 ), 123–154. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sallis JF, Owen N. Ecological models of health behavior In Glanz K, Rimer BK, Viswanath K, eds. Health Behavior: Theory, Research, and Practice . 5th ed. San Francisco, CA: Jossey Bass; 2015: 43–64. [ Google Scholar ]
• Simons-Morton B, McLeroy KR, & Wedndel ML (2012). Behavior Theory in Health Promotion Practice and Research . Burlington, MA: Jones & Bartlett Learning. [ Google Scholar ]
• Smith AL, Hantsoo L, Malykhina AP, File DW, Valentino R, Wein AJ, … &Epperson CN (2016). Basal and stress-activated hypothalamic pituitary adrenal axis function in postmenopausal women with overactive bladder . International Urogynecology Journal , 27 ( 9 ), 1383–1391. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Solar O, & Irwin A A conceptual framework for action on the social determinants of health . Social Determinants of Health Discussion Paper 2 (Policy and Practice) . 2010. [02/08/2015] [ Google Scholar ]
• Substance Abuse and Mental Health Services Administration (SAMHSA). (2017). Strategic Prevention Framework . Retrieved June 2, 2019 from https://www.gaspsdata.net/sites/default/files/spf_brochure_1.12.17_approved.pdf
• Van der Velde J, Laan E, & Everaerd W (2001). Vaginismus, a component of a general defensive reaction. An investigation of pelvic floor muscle activity during exposure to emotion-inducing film excerpts in women with and without vaginismus . International Urogynecology Journal , 12 ( 5 ), 328–331. [ PubMed ] [ Google Scholar ]
• Van Ryn M, & Heaney CA (1992). What’s the use of theory? Health Education Quarterly , 19 ( 3 ), 315–330. [ PubMed ] [ Google Scholar ]
• Vandenbroeck P, Goossens J & Clemens M (2007). Foresight, Tackling Obesities: Future Choices – Obesity System Atlas . Retrieved June 2, 2019 from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/295153/07-1177-obesity-system-atlas.pdf
• Warnecke RB, Oh A, Breen N, Gehlert S, Paskett E, Tucker KL, … & Hiatt RA. (2008). Approaching health disparities from a population perspective: the National Institutes of Health Centers for Population Health and Health Disparities . American Journal of Public Health , 98 ( 9 ), 1608–1615. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• W. K. Kellogg Foundation. (2004). Using logic models to bring together planning, evaluation, and action: logic model development guide . Michigan: Kellogg Foundation. [ Google Scholar ]
• World Health Organization, Regional Office for the Eastern Mediterranean . (1995). Constitution of the World Health Organization; https://apps.who.int/iris/handle/10665/121457 [ Google Scholar ]
• Yousefichaijan P, Sharafkhah M, Rafiei M, & Salehi B (2016). Attention-deficit/hyperactivity disorder in children with overactive bladder; a case-control study . Journal of Renal Injury Prevention , 5 ( 4 ), 193. [ PMC free article ] [ PubMed ] [ Google Scholar ]

The Research Frameworks Network

Here you can access directly the different research frameworks or cross search across the frameworks for research questions and strategies associated with different places, periods or themes. NB Page under construction!

• Explore Frameworks
• Explore By Map

National Research Frameworks

Historic england research agenda, scottish archaeological research framework, a research framework for the archaeology of wales, regional & local frameworks, east of england research framework, east midlands historic environment research framework, research framework for london archaeology, north east research framework, north west england regional research framework, south west archaeology research framework, south yorkshire historic environment research framework, west midlands research framework, worcestershire research framework, solent thames research framework, west yorkshire research agenda, greater thames estuary research framework, highland archaeological research framework, perth and kinross archaeological research framework, regional archaeological research framework for argyll, wiltshire museum research framework, the north sea prehistory research and management framework, south east regional research framework, yorkshire wolds research strategy, south east of scotland archaeological research framework, specialist frameworks, historic built environment knowledge exchange, mesolithic research and conservation framework for england, a maritime archaeological research agenda for england, archaeology of mining & quarrying in england, future thinking on carved stones in scotland, world heritage sites, derwent valley world heritage site, stonehenge and avebury world heritage site, frontiers of the roman empire world heritage site: the antonine wall, international frameworks, netherlands national archaeological research agenda (2.0), france national programme of archaeological research, denmark national archaeology strategy, flanders archaeology framework, all regions.

• Open access
• Published: 10 January 2024

A scoping review of theories, models and frameworks used or proposed to evaluate knowledge mobilization strategies

• Saliha Ziam   ORCID: orcid.org/0000-0002-8892-9572 1 ,
• Sèverine Lanoue 2 ,
• Esther McSween-Cadieux 2 ,
• Mathieu-Joël Gervais 3 ,
• Julie Lane 2 , 4 ,
• Dina Gaid 5 ,
• Laura Justine Chouinard 1 ,
• Christian Dagenais 6 ,
• Valéry Ridde 7 , 8 ,
• Emmanuelle Jean 9 ,
• France Charles Fleury 10 ,
• Quan Nha Hong 5 &
• Ollivier Prigent 2  

Health Research Policy and Systems volume  22 , Article number:  8 ( 2024 ) Cite this article

2338 Accesses

6 Altmetric

Metrics details

Evaluating knowledge mobilization strategies (KMb) presents challenges for organizations seeking to understand their impact to improve KMb effectiveness. Moreover, the large number of theories, models, and frameworks (TMFs) available can be confusing for users. Therefore, the purpose of this scoping review was to identify and describe the characteristics of TMFs that have been used or proposed in the literature to evaluate KMb strategies.

A scoping review methodology was used. Articles were identified through searches in electronic databases, previous reviews and reference lists of included articles. Titles, abstracts and full texts were screened in duplicate. Data were charted using a piloted data charting form. Data extracted included study characteristics, KMb characteristics, and TMFs used or proposed for KMb evaluation. An adapted version of Nilsen (Implement Sci 10:53, 2015) taxonomy and the Expert Recommendations for Implementing Change (ERIC) taxonomy (Powell et al. in Implement Sci 10:21, 2015) guided data synthesis.

Of the 4763 search results, 505 were retrieved, and 88 articles were eligible for review. These consisted of 40 theoretical articles (45.5%), 44 empirical studies (50.0%) and four protocols (4.5%). The majority were published after 2010 ( n  = 70, 79.5%) and were health related ( n  = 71, 80.7%). Half of the studied KMb strategies were implemented in only four countries: Canada, Australia, the United States and the United Kingdom ( n  = 42, 47.7%). One-third used existing TMFs ( n  = 28, 31.8%). According to the adapted Nilsen taxonomy, process models ( n  = 34, 38.6%) and evaluation frameworks ( n  = 28, 31.8%) were the two most frequent types of TMFs used or proposed to evaluate KMb. According to the ERIC taxonomy, activities to “train and educate stakeholders” ( n  = 46, 52.3%) were the most common, followed by activities to “develop stakeholder interrelationships” ( n  = 23, 26.1%). Analysis of the TMFs identified revealed relevant factors of interest for the evaluation of KMb strategies, classified into four dimensions: context, process, effects and impacts.

Conclusions

This scoping review provides an overview of the many KMb TMFs used or proposed. The results provide insight into potential dimensions and components to be considered when assessing KMb strategies.

Peer Review reports

Contribution to the literature

The evaluation of KMb strategies is a critical dimension of the KMb process that is still poorly documented and warrants researchers’ attention.

Our review identified the most common theories, models and frameworks (TMFs) proposed or used to assess KMb strategies and the main components to consider when evaluating a KMb strategy.

By developing an integrative reference framework, this work contributes to improving organizations’ capacity to evaluate their KMb initiatives.

It is widely recognized that research evidence has the potential to inform, guide, and improve practices, decisions, and policies [ 1 ]. Unfortunately, for diverse reasons, the best available evidence is still too seldom taken into account and used [ 2 , 3 , 4 , 5 , 6 , 7 ]. The field of research on knowledge mobilization (KMb) has been growing rapidly since the early 2000s [ 2 , 3 , 8 , 9 , 10 , 11 ]. Its purpose is to better understand how to effectively promote and support evidence use.

Knowledge mobilization is one of many terms and concepts developed over recent decades to describe processes, strategies, and actions to bridge the gap between research and practice. Other common terms often paired interchangeably with the term “knowledge” are “translation”, “transfer”, “exchange”, “sharing” and “dissemination”, among others. [ 12 , 13 ]. Some are more closely linked than others to specific fields or jurisdictions. For this study, we adopted the term knowledge mobilization (KMb) because it conveys the notions of complexity and multidirectional exchanges that characterize research-to-action processes. We used it as an umbrella concept that encompasses the efforts made to translate knowledge into concrete actions and beneficial impacts on populations [ 1 ]. Moreover, the term KMb is also used by research funding agencies in Canada to emphasize the medium- and long-term effects that research knowledge or research results can have on potential users [ 1 , 14 ].

KMb represents all processes from knowledge creation to action and includes all strategies implemented to facilitate these processes [ 14 ]. A KMb strategy is understood as a coordinated set of activities to support evidence use, such as dissemination activities to reach target audiences (for example, educational materials, practical guides, decision support tools) or activities to facilitate knowledge application in a specific context and support professional behaviour change (for example, community of practice, educational meetings, audits and feedback, reminders, deliberative dialogues) [ 15 ]. A KMb process may vary in intensity, complexity or actor engagement depending on the nature of the research knowledge and the needs and preferences of evidence users [ 7 ].

KMb is considered a complex process, in that numerous factors can facilitate or hinder its implementation and subsequent evidence use. The past two decades have seen the emergence of a deeper understanding of these factors [ 2 , 3 , 16 ]. These may be related to the knowledge mobilized (for example, relevance, reliability, clarity, costs), the individuals involved in the KMb process (for example, openness to change, values, time available, resources), the KMB strategies (for example, fit with stakeholder needs and preferences, regular interactions, trust relationships, timing), and organizational and political contexts (for example, culture of evidence use, leadership, resources) [ 2 , 6 , 17 , 18 ]. However, more studies are needed to understand which factors are more important in which contexts, and to evaluate the effects of KMb strategies.

On this last point, while essential, it is often very complex to study KMb impacts empirically to demonstrate the effectiveness of KMb strategies [ 19 , 20 , 21 ]. Partly for this reason, high-quality studies that evaluate process, mechanisms and effects of KMb strategies are still relatively rare [ 2 , 22 , 23 , 24 , 25 ]. As a result, knowledge about the effectiveness of different KMb strategies remains limited [ 10 , 17 , 19 , 23 , 26 , 27 , 28 ] and their development cannot be totally evidence informed [ 3 , 19 , 20 , 23 , 29 , 30 ], which may seem incompatible with the core values and principles of KMb.

The growing interest in KMb has led to an impressive proliferation of conceptual propositions, such as theories, models and frameworks (TMF) [ 2 , 3 , 9 , 11 , 12 , 31 , 32 ]. Many deplore the fact that these are poorly used [ 11 , 30 , 33 ] and insist on the need to test, refine and integrate existing ones [ 3 , 31 , 34 ]. Indeed, the conceptual and theoretical development of the field has outpaced its empirical development. This proliferation appears to have created confusion among certain users, such as organizations that need to evaluate their KMb strategies. Besides implementing and funding KMb strategies, knowledge organizations such as granting agencies, governments and public organizations, universities and health authorities are often required to demonstrate the impact of their strategies [ 21 , 35 , 36 ]. Yet this can be a significant challenge [ 20 , 23 , 29 ]. They may have difficulty knowing which TMFs to choose, in what context and how to use them effectively in their evaluation process [ 12 , 37 ].

Indeed, the evaluation of KMb strategies is still relatively poorly documented, with respect to the phases of their development and implementation. Our aim in this scoping review is to clarify, conceptually and methodologically, this crucial dimension of the KMb process. This would help organizations gain access to evidence-based, operational and easy-to-use evaluation toolkits for assessing the impacts of their KMb strategies.

To survey the available knowledge on evaluation practices for KMb strategies, we conducted a scoping review. According to Munn et al. [ 38 ], a scoping review is indicated to identify the types of available evidence and knowledge gaps, to clarify concepts in the literature and to identify key characteristics or factors related to a concept. This review methodology also allows for the inclusion of a diversity of publications, regardless of their nature or research design, to produce the most comprehensive evidence mapping possible [ 39 ]. The objective of the scoping review was to identify and describe the characteristics of theories, models and frameworks (TMFs) used or proposed to evaluate KMb strategies. The specific research questions were:

What TMFs to evaluate KMb strategies exist in the literature?

What KMb strategies do they evaluate (that is types of KMb objectives, activities, target audiences)?

What dimensions and components are included in these TMFs?

This scoping review was conducted based on the five steps outlined by Arksey and O’Malley [ 39 ]: (1) formulating the research questions; (2) identifying relevant studies; (3) selecting relevant studies; (4) extracting and charting data; and (5) analysing, collating, summarizing and presenting the data. Throughout the process, researchers and knowledge users (KMb practitioners) were involved in decisions regarding the research question, search strategy, selection criteria for studies and categories for data charting. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines [ 40 ]. No protocol was registered for this review.

Search strategy and information sources

The search strategy was developed, piloted and refined in consultation with our team’s librarian. Search terms included controlled vocabulary and keywords related to three main concepts: (1) knowledge mobilization (for example [knowledge or evidence or research] and transfer, translation, diffusion, dissemination, mobilization, implementation science, exchange, sharing, use, uptake, evidence-based practice, research-based evidence), (2) evaluation (for example, evaluat*, measur*, impact, outcome, assess, apprais*, indicator) and (3) TMF (for example, framework*, model*, method*, guide*, theor*). See Additional file 1 for the search terms and strategies used in the electronic searches.

The following databases were searched from January 2000 to August 2023: MEDLINE (Ovid), PsycInfo (Ovid), ERIC (ProQuest), Sociological Abstracts (ProQuest), Dissertations & Theses (Proquest), Érudit and Cairn. These databases were chosen to identify relevant references in the health, education and social fields. Several search strategies were tested by the librarian to optimize the retrieval of citations known to the investigators and to increase the likelihood that all relevant studies would be retrieved. We also searched reference lists of included articles and previous systematic reviews [ 11 , 12 , 15 , 41 ].

Eligibility criteria

A publication was considered eligible if it (1) presented or used a theory, model, or framework (TMF), (2) described dimensions or specific components to consider in the evaluation of KMb strategies, (3) presented or discussed KMb strategies or activities (any initiatives to improve evidence use), and (4) proposed outcomes that might result directly or indirectly from the KMb strategies. Studies were excluded from analysis if they (1) presented a TMF to assess the impact of research without mentioning KMb strategies or an intervention not related to KMb and (2) presented evaluation dimensions or components that could not be generalized. We considered publications in English or French. All types of articles and study designs were eligible, including study protocols.

Study selection

The results of the literature search were imported into Covidence, which the review team used for screening. After duplicate articles were removed, the titles and abstracts were screened independently by two of the three reviewers (EMC, MJG, GL). Publications identified as potentially relevant were retrieved in full text and screened independently by three reviewers (EMC, MJG, GL). Discrepancies regarding the inclusion of any publication were resolved through discussion and consensus among reviewers. The principal investigator (SZ) validated the final selection of articles.

Data synthesis

A data charting form was developed in Microsoft Excel and piloted by the research team. Data extracted included study characteristics (authors, authors’ country of affiliation, year, journal, discipline, article type, study setting, study aim), KMb strategies of interest, KMb objectives, KMb target audiences and TMFs used or proposed for KMb evaluation (existing or new TMF, specific dimensions or components of TMF and so on). Data were extracted by a single reviewer (SL, JC or OP) and validated by a second reviewer (SZ). Disagreements were discussed between reviewers and resolved by consensus. No quality appraisal of included studies was conducted, as this is optional in scoping reviews and the purpose was only to describe the content of identified TMFs [ 42 ].

Data analysis and presentation of results

Data were summarized according to study characteristics, KMb strategy characteristics (activities, objectives, target audiences), types of TMFs, and dimensions or components to consider for KMb evaluation. Disagreements during the process were discussed and resolved through consensus (SL, DG, SZ). A KMb strategy might have one or more objectives and include one or more activities. Thus, the objectives and activities of the KMb strategies extracted from the selected studies were summarized based on existing categorizations. The categorization of KMb objectives was inspired by Gervais et al. [ 15 ] and Farkas et al. [ 43 ] (Table  1 ).

The KMb activities were categorized according to the Expert Recommendations for Implementing Change (ERIC) taxonomy [ 44 ]. The activities were first classified according to the full taxonomy and then grouped into the nine categories proposed by Waltz et al. [ 45 ] (Table  2 ).

The TMFs were categorized according to the categories of theoretical approaches described by Nilsen [ 32 ]: process models, evaluation frameworks, determinant frameworks and classic theories (Table  3 ). The category “implementation theories” originally described by Nilsen [ 32 ] was not used because we did not identify any article that fit this category. We also added a category named “logic models” due to the nature of the identified TMFs. Logic models are often used in theory-driven evaluation approaches and are usually developed to show the links among inputs (resources), activities and outputs (outcomes and short-, medium- and long-term effects) [ 46 ].

Finally, the content extracted from the TMFs was analysed using mainly an inductive method. This method allows, among other things, to develop a reference framework or a model from the emerging categories that are evident in the text data [ 50 ].

The classification of concepts is the result of multiple readings and interpretations. The concepts associated with each dimension of the framework were classified according to their meaning. Similar concepts were grouped together to form components. These grouped components were then associated with the subdimensions and main dimensions of the framework.

Search results

The searches yielded 4763 articles. Of those, 4258 were excluded during the title and abstract screening. Of the 505 full-text articles, we retained 88 in our final sample. The results of the search and selection processes (PRISMA flowchart) are summarized in Fig.  1 .

PRISMA flowchart summarizing search strategy and selection results [ 40 ]

Publication characteristics

Most articles were published after 2010 ( n  = 70, 79.5%), with an average of 5 articles per year between 2010 and 2023 compared with an average of 2.1 articles per year between 2001 and 2009; there were no eligible articles from 2000. The search was conducted in August 2023, and only five articles were published in these 7 months of the year. Table 4 presents the main characteristics of the selected articles. A full list of the included articles with their main characteristics is presented in Additional file 2 .

The number of theoretical and empirical articles was relatively similar. Among the theoretical articles, 19 descriptive articles (21.6%) were aimed at describing a KMb strategy, a KMb infrastructure or a TMF related to a specific programme or context; 18 articles (20.5%) synthesized knowledge to propose a TMF (new or revised); and three articles conducted systematic reviews (3.4%).

The empirical articles category included studies with different methodological approaches (quantitative, qualitative, mixed methods). We will not report the details of the methodologies used, as this would result in a long list with few occurrences. The empirical articles can be divided into three categories: (1) studies that evaluated a TMF related to KMb ( n  = 16, 18.2%), (2) studies that evaluated a KMb strategy ( n  = 21, 23.9%) and (3) studies that evaluated both a KMb strategy and a TMF ( n  = 7, 8.0%).

Most articles were related to healthcare ( n  = 71, 80.7%). This field of study was divided into three subdomains. The healthcare and social services articles usually described or assessed a KMb strategy targeting health professionals’ practices in a variety of fields (for example, occupational therapy, dentistry, mental health, pharmacology, gerontology, nursing and so on). The health policy and systems articles usually described or assessed KMb strategies targeting decision-making processes, decision-makers or public health interventions and policies. The continuing education articles assessed training programmes for health professionals aimed at increasing knowledge and skills in a specific field. The articles in the general field described or discussed TMFs and KMb strategies that could be applied to multiple disciplines or contexts. Finally, the articles in the education field described or assessed a KMb strategy targeting education professionals.

Almost half of the articles ( n  = 42, 47.7%) studied KMB strategies implemented in only four countries: Canada, Australia, the United States and the United Kingdom. Countries in South America, the Caribbean, Africa, Asia, the Middle East, China and Europe were underrepresented ( n  = 8, 9.1%). The remaining 34 articles (38.6%) did not specify an implementation context and were mostly theoretical articles. Regarding the authors’ countries of affiliation, Canada, the United States, Australia and the United Kingdom were again the most represented countries, featuring in 85% of the articles ( n  = 75).

What theories, models or frameworks exist in the literature to evaluate KMb strategies?

Several articles proposed a new TMF ( n  = 37, 42.0%), and some articles proposed a logic model specifically developed to evaluate their KMb strategy ( n  = 17, 19.3%). One-third of the articles used existing TMFs ( n  = 28, 31.8%). A few articles only referred to existing TMFs but did not use them to guide a KMb strategy evaluation ( n  = 6, 8.5%).

The identified TMFs were then categorized according to their theoretical approaches (adapted from Nilsen, [ 32 ]) (Table  5 ). Five articles used or proposed more than one TMF, and three TMFs could be classified in two categories. Several articles proposed or used a process model ( n  = 34, 38.6%) or an evaluation framework ( n  = 28, 31.8%); these were the two most frequently identified types of TMFs. Fewer articles proposed or used a logic model ( n  = 17, 19.3%), a determinant framework ( n  = 12, 13.6%) or a classic theory ( n  = 7, 8.0%). The TMFs most often identified in the articles were the RE-AIM framework ( n  = 5, 5.7%), the Knowledge-to-Action framework [ 9 ] ( n  = 4, 4.5%), the Theory of Planned Behavior [ 51 ] ( n  = 3, 3.4%) and the Expanded Outcomes framework for planning and assessing continuing medical education [ 52 ] ( n  = 3, 3.4%). In total, we identified 87 different TMFs in the 88 articles. Only nine TMFS were retrieved in more than one article.

What KMb strategies do the TMFs evaluate (activities, objectives, target audience)?

Thirty-eight articles reported using more than one activity in their KMb strategy. According to the ERIC compilation, “Train and educate stakeholders” activities were the most common, followed by “Develop stakeholder interrelationships” and “Use evaluative and iterative strategies”. Table 6 presents the various types of activities and the number of articles that referred to each.

Of the 88 articles analysed, 18 (20.4%) did not specify a KMb objective. The remaining articles proposed one or more KMb strategy objectives. Specifically, 39 (36.4%) articles had one objective, 15 (17.0%) had two, three (3.4%) had three, and 13 (14.8%) had four or five. Table 7 presents the different types of objectives and the number of times they were identified.

The target audiences for KMb strategies were clearly specified in half of the articles ( n  = 44, 50.0%). Generally, these were empirical articles that targeted specific professionals ( n  = 36, 40.9%) or decision-makers ( n  = 8, 9.1%). Just under one-third of the articles identified a broad target audience (for example, professionals and managers in the health system, a health organization) ( n  = 26, 29.5%). Finally, 18 articles (20.4%) did not specify a target audience for KMb; these were most often theoretical articles.

What are the dimensions and components included in TMFs for evaluating KMb strategies?

The analysis of the identified TMFs revealed many factors of interest relevant for the evaluation of KMb strategies. These specific components were inductively classified into four main dimensions: context, process, effects and impacts (Fig.  2 ). The context dimension refers to the assessment of the conditions in place when the KMb strategy is implemented. These include both the external (that is, sociopolitical, economic, environmental and cultural characteristics) and internal environments (that is, characteristics of organizations, individuals and stakeholder partnerships). These factors are understood to influence the selection and tailoring of a KMb strategy. The process dimension refers to the assessment of the planning, levels and mechanisms of implementation, as well as to the characteristics of the KMb strategy implemented. The effects dimension refers to the assessment of outcomes following the KMb strategy implementation. The potential effects vary depending on the strategy’s objectives and can be either the immediate results of the KMb strategy or short-, medium- and long-term outcomes. The conceptual gradation of effects was generally represented in a similar way in the TMFs analysed, but the temporality of effects could vary. A medium-term outcome in one study could be understood as a long-term outcome in another. However, the majority of authors group these effects into three categories (Gervais et al. 2016: p. 6): (1) short-term effects, measured by success of KMb strategy measured by success of KMb strategy (number of people reached, satisfaction, participation and so on); (2) medium-term effects linked to changes in individual attitude and the use of knowledge; and (3) the long-term effects that result from achieving the KMb objective (for example, improved practices and services, changed collective behaviour, sustainable use of knowledge).

The main evaluation dimensions that emerged from the TMFs analysed

Finally, the impacts dimension refers to the ultimate effects of KMb products or interventions on end users, as measured by the organization (Phipps et al. [ 36 ], p. 34). The evaluation of these ultimate effects can be measured by the integration of a promising practice into organizational routines, by the effects on service users or by the effects on the health and well-being of communities and society in general.

This gradation shows the importance of measuring effects at different points in time, to take account of the time they take to appear and their evolving nature (Gervais et al., 2016: p. 6).

Most of the articles presented the dimensions that should be evaluated, whereas the empirical articles presented the dimensions but also used them in practice to evaluate a KMb strategy. Only five articles (5.7%) did not mention specific dimensions that could be classified.

Table 8 presents both the number of articles that presented dimensions to be evaluated and the number of articles that evaluated them in practice. These results showed that the effects dimension was both the most often named and the most evaluated in practice. The other three dimensions (context, process, impacts), while quite often mentioned as relevant to assess, were less often evaluated in practice. For example, only five articles (5.7%) reported having assessed the impacts dimension.

As previously mentioned, the components relevant for the evaluation of KMb strategies were extracted from the identified TMFs. Table 9 presents these components, which represent the more specific factors of interest for assessing context, process, effects and impacts.

Although often overlooked, the evaluation of KMb strategies is an essential step in guiding organizations seeking to determine whether the expected outcomes of their initiatives are being realized. Evaluation not only allows organizations to make adjustments if the initiatives are not producing the expected results, but also helps them to justify their funding of such initiatives. Evaluation is also essential if the KMb science is to truly inform KMb practice, such that the strategies developed are based on empirical data [ 30 ]. To make KMb evaluation more feasible, evaluation must be promoted and practices improved.

This scoping review meets the first objective of our project, which was to provide an overview of reference frameworks used or proposed for evaluating KM strategies, and to propose a preliminary version of a reference framework for evaluating KM strategies. Several key findings emerged from this scoping review:

Proliferation of theories, models and frameworks, but few frequently used

We are seeing a proliferation of TMFs in KMb and closely related fields [ 132 , 133 ]. Thus, the results of this scoping review support the argument that the conceptual and theoretical development of the field is outpacing its empirical development. Most of the reviewed articles (42.0%) proposed a new TMF rather than using existing ones. Furthermore, we identified relatively few empirical studies (50.0%) that focused on the evaluation of KMb strategies. Consequently, the TMFs used were poorly consolidated, which does not provide a solid empirical foundation to guide the evaluation of KMb strategies. Also, not all the TMFs proposed in the articles were specifically developed for evaluation; some were focused on KMb implementation processes. These may still provide elements to consider for evaluation, although they were not designed to propose specific indicators.

A scoping review published in 2018 identified 596 studies using 159 different KMb TMFs, 95 of which had been used only once [ 11 ]. Many authors reported that these are rarely reused and validated [ 11 , 30 , 33 ] and that it is important to test, refine and integrate existing ones [ 3 , 31 , 34 , 133 ]. A clear, collective and consistent use of existing TMFs is recommended and necessary to advance KMb science and closely related fields [ 12 , 31 ]. The systematic review by Strifler et al. [ 11 ] highlights the diversity of available TMFs and the difficulty users may experience when choosing TMFs to guide their KMb initiatives or evaluation process. Future work should focus on the development of tools to better support users of TMFs, especially those working in organizations. By consolidating a large number of TMFs, the results of this scoping review contribute to these efforts.

The importance of improving evaluation practices for complex multifaceted KMb strategies

Another noteworthy finding was the emphasis on the evaluation of strategies focused on education and professional training for practice improvement (52.3%). Relatively few of the reviewed articles looked at, for example, the evaluation of KMb strategies aimed at informing or influencing decision-making (13.6%), or KMb strategies targeting decision-makers (9.1%). These results reaffirm the importance of conducting more large-scale evaluations of complex and multifaceted KMb strategies. These involve a greater degree of interaction and engagement, are composed of networks of multiple actors, mobilize diverse sources of knowledge and have simultaneous multilevel objectives [ 19 , 134 ].

The fact that some KMb strategies are complex interventions implemented in complex contexts [ 134 ] presents a significant and recurring challenge to their evaluation. Methodological designs, approaches and tools are often ill-suited to capture the short-, medium- and long-term outcomes of KMb strategies, as well as to identify the mechanisms by which these outcomes were produced in a specific context. It is also difficult to link concrete changes in practice and decision-making to tangible longer-term impacts at the population level. Moreover, these impacts can take years to be achieved [ 36 ] and can be influenced by several other factors in addition to KMb efforts [ 2 , 19 , 24 ]. Comprehensive, dynamic and flexible evaluation approaches [ 135 , 136 , 137 ] using mixed methods [ 20 ] appear necessary to understand why, for whom, how, when and in what context KMb strategies achieve their objectives [ 2 , 21 , 25 ]. For instance, realist evaluation, which belongs to theory-based evaluation, may be an approach that addresses issues of causality without sacrificing complexity [ 134 , 138 , 139 ]. This evaluation approach aims to identify the underlying generative mechanisms that can explain how the outcomes were generated and what characteristics of the context affected, or not, those mechanisms. This approach is used to test and refine theory about how interventions with a similar logic of action actually work [ 139 ].

Large heterogeneity of methodologies used in empirical studies

Despite the growth of the KMb field, a recurring issue is the relatively limited number of high-quality studies that evaluate KMb outcomes and impacts. This observation is shared by many of the authors of our scoping articles [ 2 , 22 , 23 , 24 , 25 ]. Only a limited number of empirical articles met the selection criteria ( n  = 44/88) in this scoping review. Synthesizing these studies is challenging due to the diversity of research designs used and the large number of potential evaluation components identified. In addition, most of the identified studies used TMFs and measurement tools that were not validated [ 20 , 29 ] and that were specifically developed for their study [ 16 , 25 , 140 ]. Moreover, these studies did not describe the methods used to justify their choice of evaluation dimensions and components [ 25 ], which greatly hinders the ability to draw inferences and develop generalizable theories through replication in similar studies [ 110 , 140 , 141 , 142 , 143 ]. The lack of a widely used evaluation approach across the field is therefore an important issue [ 16 , 20 ] also highlighted by this scoping review.

Our aim in this review was not to identify specific indicators or measurement tools (for example, questionnaires) for assessing KMb strategies, but rather to describe dimensions and component of TMFs used for KMb evaluation. However, a recent scoping review [ 144 ] looked at measurement tools and revealed that only two general potential tools have been identified to assess KMb activities in any sector or organization: the Level of Knowledge Use Survey (LOKUS) [ 145 ] and the Knowledge Uptake and Utilization Tool (KUUT) [ 95 ]. The authors also assert the importance of developing standardized tools and evaluation processes to facilitate comparison of KMb activities’ outcomes across organizations [ 144 ].

Lack of description and reporting of KMb strategies and evaluation

Another important finding from this review was the sparsity of descriptions of KMb strategies in the published articles. In general, the authors provided little information on the operationalization of their KMb strategies (for example, objectives, target audiences, details of activities implemented, implementation context, expected effects). The KMb strategy objectives and the implemented activities should be carefully selected and empirically, theoretically or pragmatically justified before the evaluation components and specific indicators can be determined [ 146 ].

To improve consistency in the field and to contribute to the development of KMb science, many authors reported the need to better describe and report KMb strategies and their context [ 8 , 54 , 146 , 147 , 148 , 149 , 150 ]. KMb strategies are often inconsistently labelled across studies, poorly described and rarely justified theoretically [ 146 , 150 , 151 ]. It was not possible in this scoping review to associate the evaluation components to be used with the objectives and types of KMb strategies, as too much information was missing in the articles. Over the past 10 years, several guidelines have been proposed to improve the reporting of interventions such as KMb strategies: the “Workgroup for Intervention Development and Evaluation Research (WIDER) recommendations checklist” [ 147 ], the “Standards for Reporting Implementation Studies (StaRI)” [ 150 ] and the “Template for Intervention Description and Replication (TIDieR)” [ 152 ]. These guidelines should be used more often to enhance the reporting of KMb strategies and help advance the field [ 153 ].

Implications for future research

This scoping review provides an overview of potential factors of interest for assessing the context, process, effects and impacts of a KMb strategy. It also proposes a preliminary inventory of potential dimensions and components to consider when planning the evaluation of a KMb strategy. Given the broad spectrum of factors of interest identified across studies, not all of them can be assessed in every context. Rather, they should be targeted according to the objectives of the evaluation, the nature of the KMb strategy and the resources available to conduct the evaluation. Thus, this inventory should not be understood as a prescriptive, normative and exhaustive framework, but rather as a toolbox to identify the most relevant factors to include in the evaluation of a given KMB strategy, and to address a need often expressed by organizations wishing to evaluate their KMb efforts.

Additional work is needed to validate and operationalize these dimensions, to identify relevant measurement tools related to the different components and to see how this inventory could support KMb evaluation practices in organizations.

This scoping review is the first stage of a larger research project aimed at improving organizations’ capacity to evaluate their KMb initiatives by developing an integrative, interdisciplinary and easy-to-use reference framework. In the second phase of the project, the relevance and clarity of the evaluation dimensions identified in the scoping review will be validated through a Delphi study with KMb specialists and researchers. The enriched framework will then be pilot tested in two organizations carrying out and evaluating KMb strategies, to adapt the framework to their needs and to further clarify how the dimensions can be measured in practice. In this third phase, guidance will be provided to help organizations adopt the framework and its support kit. The aim of the project is to go beyond proposing a theoretical framework, and to help build organizations’ capacity to evaluate KT strategies by proposing tools adapted to their realities.

Review limitations

Some limitations of this scoping review should be acknowledged. First, given the numerous different terms used to describe and conceptualize the science of using evidence, it is possible that our search strategy did not capture all relevant publications. However, to limit this risk, we manually searched the reference lists of the selected articles. Second, the literature search was limited to articles published in English or French, and the articles were mostly from high-income countries (for example, North America); therefore, the application of the identified concepts in this scoping review to other contexts should be further explored.

In addition, the search strategy focused on scientific publications to assess progress made in the field of knowledge mobilization strategy evaluation. The grey literature was not examined. It should be considered in future research to complete the overview of evaluation needs in the field of knowledge mobilization.

Finally, the paucity of information in the articles sometimes made it difficult to classify the TMFs according to the taxonomies [ 32 , 44 ], which may have led to possible misinterpretation. However, to limit the risk of errors, the categorization was performed by two reviewers and validated by a third in cases of uncertainty.

Given the increasing demand from organizations for the evaluation of KMb strategies, along with the poorly consolidated KMb research field, a scoping review was needed to identify the range, nature and extent of the literature. This scoping review enabled us to synthesize the breadth of the literature, provide an overview of the many theories, models and frameworks used, and identify and categorize the potential dimensions and components to consider when evaluating KMb initiatives. This scoping review is part of a larger research project, in which the next steps will be to validate the integrative framework and develop a support kit to facilitate its use by organizations involved in KMb.

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article and its additional files.

Abbreviations

• Knowledge mobilization
• Theories, models, and frameworks

Social Sciences and Humanities Research Council. Guidelines for Effective Knowledge Mobilization. 2019. https://www.sshrc-crsh.gc.ca/funding-financement/policies-politiques/knowledge_mobilisation-mobilisation_des_connaissances-eng.aspx Accessed 28 Dec 2022.

Boaz A, Davies H, Fraser A, Nutley S. What works now? evidence-informed policy and practice. Bristol: Policy press; 2019.

Book   Google Scholar  

Curran JA, Grimshaw JM, Hayden JA, Campbell B. Knowledge translation research: the science of moving research into policy and practice. J Contin Educ Heal Prof. 2011;31(3):174–80.

Article   Google Scholar  

Global Commission on Evidence. The Evidence Commission report: A wake-up call and path forward for decision-makers, evidence intermediaries, and impact-oriented evidence producers. McMaster University; 2022 p. 144. https://www.mcmasterforum.org/networks/evidence-commission/report/english

Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510–20.

Article   PubMed   PubMed Central   Google Scholar  

Orton L, Lloyd-Williams F, Taylor-Robinson D, O’Flaherty M, Capewell S. The use of research evidence in public health decision making processes: systematic review. PLoS ONE. 2011;6(7): e21704.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Straus SE, Tetroe J, Graham ID, editors. Knowledge translation in health care: moving from evidence to practice. 2nd ed. Chichester, West Sussex ; Hoboken, NJ: Wiley/BMJ Books; 2013, 406

Barwick M, Dubrowski R, Petricca K. Knowledge translation: The rise of implementation. 2020; https://ktdrr.org/products/kt-implementation/KT-Implementation-508.pdf

Graham ID, Logan J, Harrison MB, Straus SE, Tetroe J, Caswell W, et al. Lost in knowledge translation: time for a map? J Continuing Educ Health Professions. 2006;26(1):13–24.

Grimshaw JM, Eccles MP, Lavis JN, Hill SJ, Squires JE. Knowledge translation of research findings. Implement Sci. 2012;7(1):50.

Strifler L, Cardoso R, McGowan J, Cogo E, Nincic V, Khan PA, et al. Scoping review identifies significant number of knowledge translation theories, models, and frameworks with limited use. J Clin Epidemiol. 2018;100:92–102.

Article   PubMed   Google Scholar  

Esmail R, Hanson HM, Holroyd-Leduc J, Brown S, Strifler L, Straus SE, et al. A scoping review of full-spectrum knowledge translation theories, models, and frameworks. Implement Sci. 2020;15(1):11.

McKibbon KA, Lokker C, Wilczynski NL, Ciliska D, Dobbins M, Davis DA, et al. A cross-sectional study of the number and frequency of terms used to refer to knowledge translation in a body of health literature in 2006: a Tower of Babel? Implement Sci. 2010;5(1):16.

Fonds de recherche du Québec. Stratégie de mobilisation des connaissances 2014–2017. 2014. https://frq.gouv.qc.ca/en/mobilization-of-knowledge/ . Accessed 28 Dec 2022.

Gervais MJ, Souffez K, Ziam S. Quel impact avons-nous ? Vers l’élaboration d’un cadre pour rendre visibles les retombées du transfert des connaissances. TUC Revue francophone de recherche sur le transfert et l’utilisation des connaissances. 2016;1(2):21.

Google Scholar  

Williams NJ, Beidas RS. Annual research review: the state of implementation science in child psychology and psychiatry: a review and suggestions to advance the field. J Child Psychol Psychiatr. 2019;60(4):430–50.

Mitton C, Adair CE, Mckenzie E, Patten SB, Perry BW. Knowledge transfer and exchange: review and synthesis of the literature. Milbank Q. 2007;85(4):729–68.

Oliver K, Innvar S, Lorenc T, Woodman J, Thomas J. A systematic review of barriers to and facilitators of the use of evidence by policymakers. BMC Health Serv Res. 2014;14(1):2.

Fazey I, Bunse L, Msika J, Pinke M, Preedy K, Evely AC, et al. Evaluating knowledge exchange in interdisciplinary and multi-stakeholder research. Glob Environ Chang. 2014;25:204–20.

Gervais MJ, Marion C, Dagenais C, Chiocchio F, Houlfort N. Dealing with the complexity of evaluating knowledge transfer strategies: guiding principles for developing valid instruments. Res Eval. 2016;25(1):62–9.

Reed MS, Bryce R, Machen R. Pathways to policy impact: a new approach for planning and evidencing research impact. Evid policy. 2018;14(3):431–58.

Kim C, Wilcher R, Petruney T, Krueger K, Wynne L, Zan T. A research utilisation framework for informing global health and development policies and programmes. Health Res Policy Sys. 2018;16(1):9.

Langer L, Tripney J, Gough D University of London, Social Science Research Unit, Evidence for Policy and Practice Information and Co-ordinating Centre. The science of using science: researching the use of research evidence in decision-making. 2016.

Rajić A, Young I, McEwen SA. Improving the utilization of research knowledge in agri-food public health: a mixed-method review of knowledge translation and transfer. Foodborne Pathog Dis. 2013;10(5):397–412.

Scarlett J, Forsberg BC, Biermann O, Kuchenmüller T, El-Khatib Z. Indicators to evaluate organisational knowledge brokers: a scoping review. Health Res Policy Syst. 2020;18(1):93.

Bornbaum CC, Kornas K, Peirson L, Rosella LC. Exploring the function and effectiveness of knowledge brokers as facilitators of knowledge translation in health-related settings: a systematic review and thematic analysis. Implement Sci. 2015;10(1):162.

Sarkies MN, Bowles KA, Skinner EH, Haas R, Lane H, Haines TP. The effectiveness of research implementation strategies for promoting evidence-informed policy and management decisions in healthcare: a systematic review. Implement Sci. 2017;12(1):132.

Scott SD, Albrecht L, O’Leary K, Ball GD, Hartling L, Hofmeyer A, et al. Systematic review of knowledge translation strategies in the allied health professions. Implement Sci. 2012;7(1):70.

Dagenais C, Malo M, Robert É, Ouimet M, Berthelette D, Ridde V. Knowledge transfer on complex social interventions in public health: a scoping study. PLoS ONE. 2013;8(12): e80233.

Davies HT, Powell AE, Nutley SM. Mobilising knowledge to improve UK health care: learning from other countries and other sectors – a multimethod mapping study. Health Serv Deliv Res. 2015;3(27):1–190.

Damschroder LJ. Clarity out of chaos: use of theory in implementation research. Psychiatry Res. 2020;283: 112461.

Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10(1):53.

Ellen ME, Panisset U, Araujo de Carvalho I, Goodwin J, Beard J. A knowledge translation framework on ageing and health. Health Policy. 2017;121(3):282–91.

Wensing M, Bosch M, Grol R. Developing and selecting interventions for translating knowledge to action. CMAJ. 2010;182(2):E85–8.

Bennet A, Bennet D, Fafard K, Fonda M, Lomond T, Messier L, et al. Knowledge mobilization in the social sciences and humanities: moving from research to action. Frost: MQI Press; 2007.

Phipps D, Cummins J, Pepler D, Craig W, Cardinal S. The Co-produced Pathway to Impact Describes Knowledge Mobilization Processes. JCES. 2016;9(1). https://jces.ua.edu/articles/258 . Accessed 17 Nov 2022.

Birken SA, Rohweder CL, Powell BJ, Shea CM, Scott J, Leeman J, et al. T-CaST: an implementation theory comparison and selection tool. Implement Sci. 2018;13(1):143.

Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143.

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

Moullin JC, Sabater-Hernandez D, Fernandez-Llimos F, Benrimoj SI. A systematic review of implementation frameworks of innovations in healthcare and resulting generic implementation framework. Health Res Policy Syst. 2015;13(101170481):16.

Pham MT, Rajić A, Greig JD, Sargeant JM, Papadopoulos A, McEwen SA. A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Res Synthesis Methods. 2014;5(4):371–85.

Farkas M, Jette AM, Tennstedt S, Haley SM, Quinn V. Knowledge dissemination and utilization in gerontology: an organizing framework. Gerontologist. 2003;43:47–56.

Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci. 2015;10(1):21.

Waltz TJ, Powell BJ, Matthieu MM, Damschroder LJ, Chinman MJ, Smith JL, et al. Use of concept mapping to characterize relationships among implementation strategies and assess their feasibility and importance: results from the Expert Recommendations for Implementing Change (ERIC) study. Implement Sci. 2015;10(1):109.

Smith JD, Li DH, Rafferty MR. The implementation research logic model: a method for planning, executing, reporting, and synthesizing implementation projects. Implement Sci. 2020;15(1):84.

Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322–7.

Kitson A, Harvey G, McCormack B. Enabling the implementation of evidence based practice: a conceptual framework. Qual Health Care. 1998;7:149–58.

Sketris IS, Carter N, Traynor RL, Watts D, Kelly K, following contributing members of the CNODES Knowledge Translation Team: Pierre Ernst JG Brenda Hemmelgarn, Colleen Metge, Michael Paterson, Robert Platt W and Gary Teare. Building a framework for the evaluation of knowledge translation for the Canadian Network for Observational Drug Effect Studies. Pharmacoepidemiol Drug Saf. 2020;29 Suppl 1(d0r, 9208369):8–25.

Thomas DR. A general inductive approach for analyzing qualitative evaluation data. Am J Eval. 2006;27(2):237–46.

Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211.

Moore DE, Green JS, Gallis HA. Achieving desired results and improved outcomes: integrating planning and assessment throughout learning activities. J Contin Educ Health Prof. 2009;29(1):1–15.

Tschida JE, Drahota A. Fidelity to the ACT SMART Toolkit: an instrumental case study of implementation strategy fidelity. Implement Sci Commun. 2023;4(1):52.

Colquhoun H, Leeman J, Michie S, Lokker C, Bragge P, Hempel S, et al. Towards a common terminology: a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. Implement Sci. 2014;9(1):781.

Bertone MP, Meessen B, Clarysse G, Hercot D, Kelley A, Kafando Y, et al. Assessing communities of practice in health policy: a conceptual framework as a first step towards empirical research. Health Res Policy Sys. 2013;11(1):39.

Gagliardi AR, Legare F, Brouwers MC, Webster F, Wiljer D, Badley E, et al. Protocol: developing a conceptual framework of patient mediated knowledge translation, systematic review using a realist approach. Implement Sci. 2011;6(101258411):25.

Sargeant J, Borduas F, Sales A, Klein D, Lynn B, Stenerson H. CPD and KT: models used and opportunities for synergy. J Contin Educ Heal Prof. 2011;31(3):167–73.

Stetler CB, Ritchie J, Rycroft-Malone J, Schultz A, Charns M. Improving quality of care through routine, successful implementation of evidence-based practice at the bedside: an organizational case study protocol using the Pettigrew and Whipp model of strategic change. Implement Sci. 2007;2(101258411):3.

Kok MO, Schuit AJ. Contribution mapping: a method for mapping the contribution of research to enhance its impact. Health Res Policy Syst. 2012;10(101170481):21.

Dadich A. From bench to bedside: methods that help clinicians use evidence-based practice. Aust Psychol. 2010;45(3):197–211.

Brown P, Bahri P. Engagement’ of patients and healthcare professionals in regulatory pharmacovigilance: establishing a conceptual and methodological framework. Eur J Clin Pharmacol. 2019;75(9):1181–92.

Article   CAS   PubMed   Google Scholar  

Dobbins M, Ciliska D, Cockerill R, Barnsley J, DiCenso A. A framework for the dissemination and utilization of research for health-care policy and practice. Worldviews Evid Based Nurs Presents Arch Online J Knowl Synthesis Nurs. 2002;9(1):149–60.

Gagliardi AR, Brouwers MC, Bhattacharyya OK. The guideline implementability research and application network (GIRAnet): an international collaborative to support knowledge exchange: study protocol. Implement Sci. 2012;7(101258411):26.

Brooks SP, Zimmermann GL, Lang M, Scott SD, Thomson D, Wilkes G, et al. A framework to guide storytelling as a knowledge translation intervention for health-promoting behaviour change. Implement sci commun. 2022;3(1):35.

Cullen L, Hanrahan K, Edmonds SW, Reisinger HS, Wagner M. Iowa implementation for sustainability framework. Implement Sci. 2022;17(1):1.

Labbé D, Mahmood A, Miller WC, Mortenson WB. Examining the impact of knowledge mobilization strategies to inform urban stakeholders on accessibility: a mixed-methods study. Int J Environ Res Public Health. 2020;17(5):1561.

Straus SE, Tetroe J, Graham ID, Zwarenstein M, Bhattacharyya O, Shepperd S. Monitoring use of knowledge and evaluating outcomes. Can Med Assoc J. 2010;182(2):E94–8.

Bennett S, Whitehead M, Eames S, Fleming J, Low S, Caldwell E. Building capacity for knowledge translation in occupational therapy: learning through participatory action research. BMC Med Educ. 2016;16(1):257.

Brown C, Rogers S. Measuring the effectiveness of knowledge creation as a means of facilitating evidence-informed practice in early years settings in one London Borough. Lond Rev Educ. 2014;12(3):245–60.

Talbott E, De Los RA, Kearns DM, Mancilla-Martinez J, Wang M. Evidence-based assessment in special education research: advancing the use of evidence in assessment tools and empirical processes. Except Child. 2023;89(4):467–87.

Rosella LC, Bornbaum C, Kornas K, Lebenbaum M, Peirson L, Fransoo R, et al. Evaluating the process and outcomes of a knowledge translation approach to supporting use of the Diabetes Population Risk Tool (DPoRT) in public health practice. Canadian J Program Eval. 2018;33(1):21–48.

Couineau AL, Forbes D. Using predictive models of behavior change to promote evidence-based treatment for PTSD. Psychol Trauma Theory Res Pract Policy. 2011;3(3):266–75.

Dufault M. Testing a collaborative research utilization model to translate best practices in pain management. Worldviews Evid Based Nurs. 2004;1:S26-32.

Beckett K, Farr M, Kothari A, Wye L, le May A. Embracing complexity and uncertainty to create impact: exploring the processes and transformative potential of co-produced research through development of a social impact model. Health Res Policy Syst. 2018;16(1):118.

Kramer DM, Wells RP, Carlan N, Aversa T, Bigelow PP, Dixon SM, et al. Did you have an impact? A theory-based method for planning and evaluating knowledge-transfer and exchange activities in occupational health and safety. Int J Occup Saf Ergon. 2013;19(1):41–62.

Duhamel F, Dupuis F, Turcotte A, Martinez AM, Goudreau J. Integrating the illness beliefs model in clinical practice: a family systems nursing knowledge utilization model. J FAM NURS. 2015;21(2):322–48.

Wimpenny P, Johnson N, Walter I, Wilkinson JE. Tracing and identifying the impact of evidence-use of a modified pipeline model. Worldviews Evid Based Nurs. 2008;5(1):3–12.

Ward V, Smith S, House A, Hamer S. Exploring knowledge exchange: a useful framework for practice and policy. Soc Sci Med. 2012;74(3):297–304.

Grooten L, Vrijhoef HJM, Alhambra-Borras T, Whitehouse D, Devroey D. The transfer of knowledge on integrated care among five European regions: a qualitative multi-method study. BMC Health Serv Res. 2020;20(1):11.

Stetler CB. Updating the Stetler Model of research utilization to facilitate evidence-based practice. Nurs Outlook. 2001;49(6):272–9.

Ward V. Why, whose, what and how? A framework for knowledge mobilisers. Evid Policy J Res Debate Pract. 2017;13(3):477–97.

Levin RF, Fineout-Overholt E, Melnyk BM, Barnes M, Vetter MJ. Fostering evidence-based practice to improve nurse and cost outcomes in a community health setting: a pilot test of the advancing research and clinical practice through close collaboration model. Nurs Adm Q. 2011;35(1):21–33.

Currie M, King G, Rosenbaum P, Law M, Kertoy M, Specht J. A model of impacts of research partnerships in health and social services. Eval Program Plann. 2005;28(4):400–12.

Richard L, Chiocchio F, Essiembre H, Tremblay MC, Lamy G, Champagne F, et al. Communities of practice as a professional and organizational development strategy in local public health organizations in Quebec, Canada: an evaluation model. Healthc Policy. 2014;9(3):26–39.

PubMed   PubMed Central   Google Scholar  

Rycroft-Malone J, Wilkinson J, Burton CR, Harvey G, McCormack B, Graham I, et al. Collaborative action around implementation in collaborations for leadership in applied health research and care: towards a programme theory. J Health Serv Res Policy. 2013;18(3 Suppl):13–26.

Gagliardi AR, Fraser N, Wright FC, Lemieux-Charles L, Davis D. Fostering knowledge exchange between researchers and decision-makers: exploring the effectiveness of a mixed-methods approach. Health Policy. 2008;86(1):53–63.

Paquette-Warren J, Harris SB, Naqshbandi Hayward M, Tompkins JW. Case study of evaluations that go beyond clinical outcomes to assess quality improvement diabetes programmes using the Diabetes Evaluation Framework for Innovative National Evaluations (DEFINE). J Eval Clin Pract. 2016;22(5):644–52.

Paquette-Warren J, Tyler M, Fournie M, Harris SB. The diabetes evaluation framework for innovative national evaluations (DEFINE): construct and content validation using a modified Delphi method. Can J diabetes. 2017;41(3):281–96.

Abbot ML, Lee KK, Rossiter MJ. Evaluating the effectiveness and functionality of professional learning communities in adult ESL Programs. TESL Canada J. 2018;35(2):1–25.

Ho K, Bloch R, Gondocz T, Laprise R, Perrier L, Ryan D, et al. Technology-enabled knowledge translation: frameworks to promote research and practice. J Contin Educ Heal Prof. 2004;24(2):90–9.

Yu X, Hu D, Li N, Xiao Y. Comprehensive evaluation on teachers’ knowledge sharing behavior based on the improved TOPSIS method. Comput Intell Neurosci. 2022;2022(101279357):2563210.

Arora S, Kalishman SG, Thornton KA, Komaromy MS, Katzman JG, Struminger BB, et al. Project ECHO: a telementoring network model for continuing professional development. J Contin Educ Health Prof. 2017;37(4):239–44.

Smidt A, Balandin S, Sigafoos J, Reed VA. The Kirkpatrick model: a useful tool for evaluating training outcomes. J Intellect Dev Disabil. 2009;34(3):266–74.

Jeffs L, Sidani S, Rose D, Espin S, Smith O, Martin K, et al. Using theory and evidence to drive measurement of patient, nurse and organizational outcomes of professional nursing practice. Int J Nurs Pract. 2013;19(2):141–8.

Skinner K. Developing a tool to measure knowledge exchange outcomes. Can J Program Eval. 2007;22(1):49–75.

Lavis J, Ross S, McLeod C, Gildiner A. Measuring the impact of health research. J Health Serv Res Policy. 2003;8(3):165–70.

Boyko JA, Lavis JN, Abelson J, Dobbins M, Carter N. Deliberative dialogues as a mechanism for knowledge translation and exchange in health systems decision-making. Soc Sci Med. 2012;75(11):1938–45.

Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76.

Gainforth HL, Latimer-Cheung AE, Athanasopoulos P, Martin Ginis KA. Examining the feasibility and effectiveness of a community-based organization implementing an event-based knowledge mobilization initiative to promote physical activity guidelines for people with spinal cord injury among support personnel. Health Promot Pract. 2015;16(1):55–62.

Glasgow RE, Harden SM, Gaglio B, Rabin B, Smith ML, Porter GC, et al. RE-AIM planning and evaluation framework: adapting to new science and practice with a 20-year review. Front Public Health. 2019. https://doi.org/10.3389/fpubh.2019.00064 .

Shelton RC, Chambers DA, Glasgow RE. An extension of RE-AIM to enhance sustainability: addressing dynamic context and promoting health equity over time. Front Public Health. 2020;8(101616579):134.

Bender BG, Simmons B, Konkoly N, Liu AH. The asthma toolkit bootcamp to improve rural primary care for pediatric asthma. J Allergy Clin Immunol Pract. 2021;9(8):3091-3097.e1.

de la Garza Iga FJ, Mejia Alvarez M, Cockroft JD, Rabin J, Cordon A, Elias Rodas DM, et al. Using the project ECHO TM model to teach mental health topics in rural Guatemala: an implementation science-guided evaluation. Int J Soc Psychiatry. 2023;69(8):2031–41.

Alkin M, Taut S. Unbundling evaluation use. Stud Educ Eval. 2003;29(1):1–12.

Varallyay NI, Langlois EV, Tran N, Elias V, Reveiz L. Health system decision-makers at the helm of implementation research: development of a framework to evaluate the processes and effectiveness of embedded approaches. Health Res Policy Syst. 2020;18(1):64.

McCabe KE, Wallace A, Crosland A. A model for collaborative working to facilitate knowledge mobilisation in public health. Evid Policy. 2015;11(4):559–76.

Gonzales R, Handley MA, Ackerman S, O’sullivan PS. A framework for training health professionals in implementation and dissemination science. Acad Med. 2012;87(3):271–8.

Edgar L, Herbert R, Lambert S, MacDonald JA, Dubois S, Latimer M. The joint venture model of knowledge utilization: a guide for change in nursing. Nurs Leadersh. 2006;9(2):41–55.

Stetler CB, Damschroder LJ, Helfrich CD, Hagedorn HJ. A Guide for applying a revised version of the PARIHS framework for implementation. Implement Sci. 2011;6(101258411):99.

Brennan SE, Cumpston M, Misso ML, McDonald S, Murphy MJ, Green SE. Design and formative evaluation of the policy liaison initiative: a long-term knowledge translation strategy to encourage and support the use of cochrane systematic reviews for informing. Evid Policy. 2016;12(1):25–52.

Hinchcliff R, Senserrick T, Travaglia J, Greenfield D, Ivers R. The enhanced knowledge translation and exchange framework for road safety: a brief report on its development and potential impacts. Inj Prev. 2017;23(2):114–7.

Ye J, Woods D, Bannon J, Bilaver L, Kricke G, McHugh M, et al. Identifying contextual factors and strategies for practice facilitation in primary care quality improvement using an informatics-driven model: framework development and mixed methods case study. JMIR Hum Factors. 2022;9(2): e32174.

Brangan J, Quinn S, Spirtos M. Impact of an evidence-based practice course on occupational therapist’s confidence levels and goals. Occup Ther Health Care. 2015;29(1):27–38.

Bonetti D, Johnston M, Pitts NB, Deery C, Ricketts I, Tilley C, et al. Knowledge may not be the best target for strategies to influence evidence-based practice: using psychological models to understand RCT effects. Int J Behav Med. 2009;16(3):287–93.

Buckley LL, Goering P, Parikh SV, Butterill D, Foo EKH. Applying a “stages of change” model to enhance a traditional evaluation of a research transfer course. J Eval Clin Pract. 2003;9(4):385–90.

Boyko JA, Lavis JN, Dobbins M, Souza NM. Reliability of a tool for measuring theory of planned behaviour constructs for use in evaluating research use in policymaking. Health Res Policy Syst. 2011;24(9):29.

Imani-Nasab MH, Yazdizadeh B, Salehi M, Seyedin H, Majdzadeh R. Validity and reliability of the Evidence Utilisation in Policymaking Measurement Tool (EUPMT). Health Res Policy Syst. 2017;15(1):66.

Dwan KM, McInnes P, Mazumdar S. Measuring the success of facilitated engagement between knowledge producers and users: a validated scale. Evid Policy. 2015;11(2):239–52.

Haynes A, Rowbotham S, Grunseit A, Bohn-Goldbaum E, Slaytor E, Wilson A, et al. Knowledge mobilisation in practice: an evaluation of the Australian Prevention Partnership Centre. Health Res Policy Sys. 2020;18(1):13.

Haines M, Brown B, Craig J, D’Este C, Elliott E, Klineberg E, et al. Determinants of successful clinical networks: the conceptual framework and study protocol. Implement Sci. 2012;7(101258411):16.

Ko LK, Jang SH, Friedman DB, Glanz K, Leeman J, Hannon PA, et al. An application of the science impact framework to the cancer prevention and control research network from 2014–2018. Prev Med. 2019;12: 105821.

Leeman J, Sommers J, Vu M, Jernigan J, Payne G, Thompson D, et al. An evaluation framework for obesity prevention policy interventions. Prev Chronic Dis. 2012;9(101205018):E120.

Pettman TL, Armstrong R, Waters E, Allender S, Love P, Gill T, et al. Evaluation of a knowledge translation and exchange platform to advance non-communicable disease prevention. Evid Policy. 2016;12(1):109–26.

Yearwood AC. Applying a logical theory of change for strengthening research uptake in policy: a case study of the Evidence Informed Decision Making Network of the Caribbean. Rev Panam Salud Publica. 2018;42: e91.

Thomson D, Brooks S, Nuspl M, Hartling L. Programme theory development and formative evaluation of a provincial knowledge translation unit. Health Res Policy Syst. 2019;17(1):40.

Garad R, Kozica-Olenski S, Teede HJ. Evaluation of a center of research excellence in polycystic ovary syndrome as a large-scale collaborative research translation initiative, including evaluating translation of guideline impact. Semin Reprod Med. 2018;36(1):42–9.

Reddy S, Wakerman J, Westhorp G, Herring S. Evaluating impact of clinical guidelines using a realist evaluation framework. J Eval Clin Pract. 2015;21(6):1114–20.

Van Eerd D, Moser C, Saunders R. A research impact model for work and health. Am J Ind Med. 2021;64(1):3–12.

Yip O, Huber E, Stenz S, Zullig LL, Zeller A, De Geest SM, et al. A contextual analysis and logic model for integrated care for frail older adults living at home: The INSPIRE Project. Int J Integr Care. 2021;21(2):9.

Guo R, Bain BA, Willer J. Application of a logic model to an evidence-based practice training program for speech-language pathologists and audiologists. J Allied Health. 2011;40(1):e23–8.

PubMed   Google Scholar  

McDonald S, Turner T, Chamberlain C, Lumbiganon P, Thinkhamrop J, Festin MR, et al. Building capacity for evidence generation, synthesis and implementation to improve the care of mothers and babies in South East Asia: methods and design of the SEA-ORCHID Project using a logical framework approach. BMC Med Res Methodol. 2010;10(100968545):61.

Tabak RG, Khoong EC, Chambers DA, Brownson RC. Bridging research and practice: models for dissemination and implementation research. Am J Prev Med. 2012;43(3):337–50.

Wensing M, Grol R. Knowledge translation in health: how implementation science could contribute more. BMC Med. 2019;17(1):88.

Kreindler SA. Advancing the evaluation of integrated knowledge translation. Health Res Policy Sys. 2018;16(1):104.

Best A, Holmes B. Systems thinking, knowledge and action: towards better models and methods. Evid Policy. 2010;6(2):145–59.

van Mil HGJ, Foegeding EA, Windhab EJ, Perrot N, van der Linden E. A complex system approach to address world challenges in food and agriculture. Trends Food Sci Technol. 2014;40(1):20–32.

Wehrens R. Beyond two communities – from research utilization and knowledge translation to co-production? Public Health. 2014;128(6):545–51.

Ridde V, Pérez D, Robert E. Using implementation science theories and frameworks in global health. BMJ Glob Health. 2020;5(4): e002269.

Salter KL, Kothari A. Using realist evaluation to open the black box of knowledge translation: a state-of-the-art review. Implement Sci. 2014;9(1):115.

Van Eerd D, Cole D, Keown K, Irvin E, Kramer D, Gibson B, et al. Report on knowledge transfer and exchange practices: A systematic review of the quality and types of instruments used to assess KTE implementation and impact. Toronto: Institute for Work & Health; 2011 p. 130. https://www.iwh.on.ca/sites/iwh/files/iwh/reports/iwh_sys_review_kte_evaluation_tools_2011_rev.pdf

Dobbins M, Robeson P, Ciliska D, Hanna S, Cameron R, O’Mara L, et al. A description of a knowledge broker role implemented as part of a randomized controlled trial evaluating three knowledge translation strategies. Implement Sci. 2009;4(1):23.

Rychetnik L, Bauman A, Laws R, King L, Rissel C, Nutbeam D, et al. Translating research for evidence-based public health: key concepts and future directions. J Epidemiol Community Health. 2012;66(12):1187–92.

Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, et al. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017;12(1):108.

Bhawra J, Skinner K. Examination of tools associated with the evaluation of knowledge uptake and utilization: a scoping review. Eval Program Plann. 2020;83: 101875.

Lane JP, Stone VI, Nobrega A, Tomita M. Level Of Knowledge Use Survey (LOKUS): a validated instrument for tracking knowledge uptake and use. Stud Health Technol Inform. 2015;217:106–10.

Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Implement Sci. 2013;8(1):139.

Albrecht L, Archibald M, Arseneau D, Scott SD. Development of a checklist to assess the quality of reporting of knowledge translation interventions using the Workgroup for Intervention Development and Evaluation Research (WIDER) recommendations. Implement Sci. 2013;8(1):52.

Bragge P, Grimshaw JM, Lokker C, Colquhoun H, Albrecht L, Baron J, et al. AIMD - a validated, simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. BMC Med Res Methodol. 2017;17(1):38.

Kastner M, Makarski J, Hayden L, Lai Y, Chan J, Treister V, et al. Improving KT tools and products: development and evaluation of a framework for creating optimized, Knowledge-activated Tools (KaT). Implement Sci Commun. 2020;1(1):47.

Pinnock H, Barwick M, Carpenter CR, Eldridge S, Grandes G, Griffiths CJ, et al. Standards for Reporting Implementation Studies (StaRI) Statement. BMJ. 2017;6(356): i6795.

Lokker C, McKibbon KA, Colquhoun H, Hempel S. A scoping review of classification schemes of interventions to promote and integrate evidence into practice in healthcare. Implement Sci. 2015;10(1):27.

Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;7(348): g1687.

Wilson PM, Sales A, Wensing M, Aarons GA, Flottorp S, Glidewell L, et al. Enhancing the reporting of implementation research. Implement Sci. 2017;12(1):13.

Download references

Acknowledgements

We wish to thank Julie Desnoyers for designing and implementing the search strategy, Gabrielle Legendre for her contribution in the screening phase and Karine Souffez and Caroline Tessier for their input during the project.

This project was supported by an Insight Grant from the Social Sciences and Humanities Research Council of Canada (SSHRC) and by the Équipe RENARD (FRQ-SC). The funding bodies had no role in the conduct of this scoping review.

Author information

Authors and affiliations.

School of Business Administration, Université TÉLUQ, Montreal, Canada

Saliha Ziam & Laura Justine Chouinard

Department of School and Social Adaptation Studies, Faculty of Education, Université de Sherbrooke, Sherbrooke, Canada

Sèverine Lanoue, Esther McSween-Cadieux, Julie Lane & Ollivier Prigent

Department of Psychology, Université du Québec à Montréal, Montreal, Canada

Mathieu-Joël Gervais

Centre RBC d’expertise Universitaire en Santé Mentale, Université de Sherbrooke, Sherbrooke, Canada

School of Rehabilitation, Faculty of Medicine, Université de Montréal, Montreal, Canada

Dina Gaid & Quan Nha Hong

Department of Psychology, Université de Montréal, Montreal, Canada

Christian Dagenais

Université Paris Cité, IRD (Institute for Research on Sustainable Development, CEPED, Paris, France

Valéry Ridde

Institute of Health and Development (ISED), Cheikh Anta Diop University, Dakar, Senegal

Public Health Intelligence and Knowledge Translation Division, Public Health Agency of Canada, Ottawa, Canada

Emmanuelle Jean

Coordinator of the Interregional Consortium of Knowledge in Health and Social Services (InterS4), Rimouski, Canada

France Charles Fleury

You can also search for this author in PubMed   Google Scholar

Contributions

SZ, MJG, EMC, JL, CD, EJ, KS, VR and CT were involved in developing and designing the scoping review. EMC, MJG and GL (collaborator) screened articles in duplicate. SL, DG, LJC and OP extracted data from the included articles. SL and DG synthesized the data. SL, SZ and EMC drafted the manuscript. SZ led the project, supervised and assisted the research team at every stage, and secured the funding. All authors provided substantive feedback and approved the manuscript prior to submission.

Corresponding author

Correspondence to Saliha Ziam .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

Keywords and search strategy.

Additional file 2.

Summary of included articles.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Ziam, S., Lanoue, S., McSween-Cadieux, E. et al. A scoping review of theories, models and frameworks used or proposed to evaluate knowledge mobilization strategies. Health Res Policy Sys 22 , 8 (2024). https://doi.org/10.1186/s12961-023-01090-7

Download citation

Received : 16 June 2023

Accepted : 07 December 2023

Published : 10 January 2024

DOI : https://doi.org/10.1186/s12961-023-01090-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Knowledge translation
• Scoping review

Health Research Policy and Systems

ISSN: 1478-4505

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• NIH Grants & Funding
• Blog Policies

NIH Extramural Nexus

Preparing for Funding Opportunities Using the Simplified Review Framework 

Last October, we announced that NIH was implementing a simplified review framework for most research project grants (RPGs). As a reminder, in the simplified review framework NIH aims to better facilitate the mission of scientific peer review – identification of the strongest, highest-impact research. The changes are intended to:

• Enable peer reviewers to better focus on answering the key questions necessary to assess the scientific and technical merit of proposed research projects: Can and should the proposed research project be conducted?
• Mitigate the effect of reputational bias by refocusing the evaluation of investigator/environment to within the context of the proposed research.
• Reduce reviewer burden by shifting policy compliance activities to NIH staff.

Today, NIH released a Guide Notice ( NOT-OD-24-085) to provide an update on our implementation plans for the simplified review framework. The Notice provides guidance to applicants on navigating new and updated funding opportunities expected to be published between now and January 2025.

Prior to January, the community will notice that many funding opportunities for applicable activity codes are being reissued to include the simplified review framework language in Section V. Application Review Information. Active funding opportunities with due dates on or after January 25, 2025, will be expired early and most will be reissued to reflect the simplified review framework. Funding opportunities with pending expiration dates prior to January 25, 2025, including impacted parent announcements, may be extended with additional due dates until they can be reissued with the simplified review framework.

Applicants should pay close attention to the Related Notices and Key Dates sections of funding opportunities to ensure they are applying to the right opportunity for their target due date. To stay informed of notices and funding opportunity reissuances, we encourage you to monitor the NIH Guide for Grants and Contracts, where any changes will be published (you can also subscribe to a weekly digest of new NIH Guide posts).

Please note that although there are no application changes associated with the simplified review framework, NIH is transitioning to updated application forms (FORMS-I) to support other initiatives. FORMS-I will be required for due dates on or after January 25, 2025 (See these Notices for FORMS-I and Changes Coming in 2025 , as well as this blog on 2025 changes ). New and reissued funding opportunities using the simplified review framework may initially be posted without an application forms package. In these cases, application forms will be added 30 – 60 days prior to the first application due date. Applicants can begin drafting their application attachments before FORMS-I becomes available using the current FORMS-H instructions and adjust if needed once FORMS-I instructions are available.

We encourage applicants to review today’s Guide Notice for more details on NIH’s implementation plans, as well as register for a public webinar on April 17 from 1 – 2pm EDT to learn more about navigating these changes and have an opportunity to get their questions answered.

We have provided various resources on our Simplified Review Framework web page to help the community understand the changes associated with the simplified review framework, including a summary statement mockup, critique template , drop-in slides , and a recording of the November online briefing. Applicants and reviewers can expect to receive plenty of guidance and support as we near January 2025.

RELATED NEWS

Before submitting your comment, please review our blog comment policies.

Your email address will not be published. Required fields are marked *

College & Research Libraries News  ( C&RL News ) is the official newsmagazine and publication of record of the Association of College & Research Libraries,  providing articles on the latest trends and practices affecting academic and research libraries.

C&RL News  became an online-only publication beginning with the January 2022 issue.

C&RL News  Reader Survey

Give us your feedback in the 2024  C&RL News   reader survey ! The survey asks a series of questions today to gather your thoughts on the contents and presentation of the magazine and should only take approximately 5-7 minutes to complete. Thank you for taking the time to provide your feedback and suggestions for  C&RL News , we greatly appreciate and value your input.

Robin Ewing is the Collections Strategist Librarian at St. Cloud State University, email: [email protected] .

Alison Lehner-Quam is the Education Librarian at Lehman College, email: [email protected] .

Amy James is the Online Librarian for Education and Information Literacy at Baylor University, email: [email protected] .

Margaret Gregor is the Instructional Materials Center Librarian at Appalachian State University, email: [email protected] .

James Rosenzweig is the Education and Children’s Studies Librarian at Eastern Washington University, email: [email protected] .

Jennifer Ditkoff is the Head Librarian at the Dundalk location for the Community College of Baltimore County, email: [email protected] .

ALA JobLIST

Advertising Information

• Preparing great speeches: A 10-step approach (211526 views)
• The American Civil War: A collection of free online primary sources (197953 views)
• 2018 top trends in academic libraries: A review of the trends and issues affecting academic libraries in higher education (77572 views)

Perspectives on the Framework

Robin Ewing, Alison Lehner-Quam, Amy James, Margaret Gregor, James Rosenzweig, and Jennifer Ditkoff

Teacher Education and Information Literacy

Introducing the Instruction for Educators Companion Document

Robin Ewing is the Collections Strategist Librarian at St. Cloud State University, email: [email protected] . Alison Lehner-Quam is the Education Librarian at Lehman College, email: [email protected] . Amy James is the Online Librarian for Education and Information Literacy at Baylor University, email: [email protected] . Margaret Gregor is the Instructional Materials Center Librarian at Appalachian State University, email: [email protected] . James Rosenzweig is the Education and Children’s Studies Librarian at Eastern Washington University, email: [email protected] . Jennifer Ditkoff is the Head Librarian at the Dundalk location for the Community College of Baltimore County, email: [email protected] .

© 2024 Robin Ewing, Alison Lehner-Quam, Amy James, Margaret Gregor, James Rosenzweig, and Jennifer Ditkoff

T he ACRL Education and Behavioral Sciences Section (EBSS) Instruction for Educators Committee (IFE Committee) is charged “to make distinctive contributions as education library specialists to the field of bibliographic instruction.” 1 Beginning in fall 2020, members of the IFE Committee worked to create an ACRL Framework for Information Literacy for Higher Education (Framework) companion document for the field of teacher education. The Companion Document to the ACRL Framework for Information Literacy for Higher Education: Instruction for Educators (Companion Document), 2 approved by the ACRL Board of Directors in June 2023, is designed to provide support for teacher preparation programs to develop educator research skills and pedagogical praxis in the realm of information literacy. The EBSS IFE Committee examined key literature and professional standards and created a draft document within ACRL’s LibGuides, 3 which was revised after receiving many rounds of feedback from librarians and educators in the field. This article shares the process involved in creating the Companion Document, a theoretical overview within a disciplinary context, practical ways to teach the content, and an exploration of next steps for the implementation of the Companion Document.

Background and Disciplinary Context

Librarians who teach information literacy to students studying to become educators are supporting students’ development in three areas: teacher preparation and education, teacher professional practice, and teacher pedagogy practice. Librarians design and prepare instruction to (1) support teacher education students’ coursework in their teacher education program, (2) prepare teachers for research skills needed in their careers, and (3) prepare teachers to support the information needs and practices of their students. 4 The Framework for Information Literacy, along with inquiry and reflection practices, can deepen students’ understanding of research practice and knowledge within the disciplines. 5 Librarians who work with education students and within social science fields offer research experiences that extend into professional practice, such as supporting research for evidence-based classroom practice 6,7 and fostering and guiding K–12 students’ information literacy skills and dispositions. 8 The connections between teacher education and information literacy highlight the need for a companion document.

Figure 1. Screenshot from the Companion Document to the ACRL Framework for Information Literacy for Higher Education: Instruction for Educators LibGuide.

The IFE Committee created the Information Literacy Standards for Teacher Education 9 between 2006 and 2011 and linked that document to the 2000 ACRL Information Literacy Competency Standards for Higher Education. 10 In 2020, the IFE Committee considered revising the document to align with the ACRL Framework for Information Literacy. Communication with EBSS leadership and the ACRL Information Literacy Frameworks and Standards Committee, along with IFE Committee member discussion, reinforced a need to produce a new document focused on the Framework.

Companion Document Creation

After deciding a new document focused on the Framework was needed, the IFE Committee identified the steps needed to complete the Companion Document. They decided to work in small teams, which ensured no major task was assigned to a single person. The committee’s work started with a review of ACRL guidance on creating companion documents. 11,12 They also consulted the chairs of the EBSS Social Work Committee and Communication Studies Committee on their processes for developing companion documents for social work and journalism. A committee team used this information to create a project plan for the Companion Document creation.

The first section of the project plan was an environmental scan. The committee wanted to know how librarians integrate the Framework as they work with teacher education students and education faculty. They hosted the discussion “Teaching the Teachers: A Collaborative Discussion on the Framework and Standards for Teacher Education Students” 13 on November 13, 2020. After the discussion, a literature review on the intersection of teacher education and the Framework was conducted. As the committee reviewed the search results, they determined whether each resource aligned with specific frames. The committee also considered the reviewed literature through the lenses of social justice, metacognition, and digital/media literacy, key concepts in teacher education.

An essential feature of the project plan was multiple drafts based on feedback from education librarians. The committee used the feedback from the discussion and the literature review analysis to create the first draft of the Companion Document in LibGuides. The committee divided into three teams, with each team assigned two frames to draft. As part of this work, the teams reviewed the Interstate Teacher Assessment and Support Consortium (InTASC) Model Core Teaching Standards 14 and the International Society for Technology in Education (ISTE) Standards for Educators 15 to identify where those standards aligned with their assigned frames. The first draft of three frames was completed by June 2021. New committee members joined the teams in fall 2021. A polished draft of all frames was finished in time to share with participants before the discussion event “The ACRL Framework and Teacher Education: Shaping the Companion Document for Instruction for Education,” 16 held on December 10, 2021. Participants provided essential feedback on each frame.

Based on that feedback, the IFE Committee substantially revised the Companion Document. In particular, discussion participants asked for more emphasis on how to integrate the frames and education standards into instruction. This suggestion prompted an overhaul of the sample objectives and activities sections of each frame. The next version of the document was shared more widely within EBSS. Additionally, the EBSS Equity, Diversity, and Inclusion Task Force was asked to review the document with a social justice lens. These two rounds of feedback led to the next version. After review by the EBSS leadership, the Information Literacy Frameworks and Standards Committee, and the ACRL Standards Committee, the ACRL Board of Directors approved the Companion Document in June 2023.

Companion Document Example

The Companion Document is divided into sections corresponding to the six frames in the Framework for Information Literacy. Within each frame there is a section titled “In an Education Context.” This section articulates which information literacy knowledge practices and dispositions are relevant to each of the three teacher roles (as teacher education student, as professional, and as classroom teacher). The relationships between the three teacher roles and the frames are demonstrated in this example from Scholarship as Conversation:

In their course assignments, teacher education students need to be able to:

• demonstrate their ability to trace the history of a given scholarly conversation using citations; and
• summarize changes in educational scholarly perspectives over time on a particular topic. 17

Scholarship as Conversation also applies to a teacher’s professional practice, where they need to be able to:

• inform themselves about new ideas and understandings in teaching and education through their reading, their use of digital tools (e.g., journal and search alerts), and their participation in learning networks; and
• use their newfound knowledge to improve their own professional teaching practice. 18

Librarians working with teacher education students can prepare them for their work in PK–12 classrooms so that as teachers, they are ready to:

• invite students to respond to diverse perspectives by constructing their own arguments while crediting the authors and creators of the works to which they are responding; and
• encourage students to develop their own voice and to share their own knowledge, creative works, and inquiry findings with others. 19

The Companion Document, as a whole, helps librarians working with teacher education students to provide support for lifelong learning for educators.

Now that the Companion Document has been approved by the ACRL Board of Directors and published, the IFE Committee can assist librarians with the application of the Framework and the challenge of relating the Companion Document to state education standards. The IFE Committee also understands any document of this length and complexity will remain a work in progress. Given the success of the IFE discussion forums in 2020 and 2021, as well as the 2022 event “Fulfilling the Framework: Strategies for Activating Information Literacy Skills for Pre-service Educators,” 20 the IFE Committee anticipates continuing to facilitate online conversations with librarians and educators that will employ the document as a resource while also gathering feedback on the ways it can continue to improve. These discussions will inform future work to ensure the document serves the broadest possible array of potential users. Research into the Companion Document will also add to the limited literature available on teacher education, and the Framework will be used to inform future revisions of the document.

Based on conversations from the forums, the IFE Committee observed that librarians in this field expressed a greater need for instructional activities and assessments that implement the Framework successfully. A concerted effort to develop a larger collection of example lessons, whether by developing them in-house through the work of IFE Committee members or by soliciting and curating lessons from EBSS membership (or other ACRL sections), would benefit users of the Companion Document.

In reflecting on its work in recent years, the IFE Committee is delighted to see its goal reach fruition in sharing the Companion Document with the wider ACRL community. This could not have been accomplished without the generous contributions of the librarians and educators who participated in the discussion forums and feedback surveys. Their insights were critically important to the revision of the Companion Document. In engaging in this work, the IFE Committee learned much from the ACRL groups that had already developed companion documents for the Framework and is therefore ready to support others in the creation of companion documents. The Companion Document is a major contribution to the field of information literacy instruction in teacher education because it integrates the Framework into the work of educators at every level.

• “EBSS Instruction for Educators Committee,” ACRL/EBSS, accessed December 20, 2023, https://www.ala.org/acrl/ebss/acr-ebsbie .
• ACRL/EBSS Instruction for Educators Committee, “Companion Document to the ACRL Framework for Information Literacy for Higher Education: Instruction for Educators,” June 2023, https://www.ala.org/acrl/sites/ala.org.acrl/files/content/standards/Framework_Companion_Instruction_Educators.pdf .
• “Companion Document to the ACRL Framework for Information Literacy for Higher Education: Instruction for Educators,” ACRL/EBSS/IFE, accessed December 20, 2023, https://acrl.libguides.com/ed .
• Alison Lehner-Quam and Wesley Pitts, “Exploring Innovative Ways to Incorporate the Association of College and Research Libraries Framework in Graduate Science Teacher Education ePortfolio Projects,” New Review of Academic Librarianship 25, no. 2–4 (2019): 357–80, https://doi.org/10.1080/13614533.2019.1621186 .
• Sara D. Miller, “Diving Deep: Reflective Questions for Identifying Tacit Disciplinary Information Literacy Knowledge Practices, Dispositions, and Values through the ACRL Framework for Information Literacy,” Journal of Academic Librarianship 44, no. 3 (2018): 412–18, https://doi.org/10.1016/j.acalib.2018.02.014 .
• Andrej Šorgo and Jasmina Heric, “Motivational and Demotivational Factors Affecting a Teacher’s Decision on Whether to Do Research,” Center for Educational Policy Studies Journal 10, no. 3 (2020): 77–97, https://doi.org/10.26529/cepsj.869 .
• Tricia Bingham, Josie Wirjapranata, and Shirley-Ann Chinnery, “Merging Information Literacy and Evidence-Based Practice for Social Work Students,” New Library World 117, nos. 3–4 (2016): 201–13, https://doi.org/10.1108/NLW-09-2015-0067 .
• Di Wu, Chi Zhou, Yating Li, and Min Chen, “Factors Associated with Teachers’ Competence to Develop Students’ Information Literacy: A Multilevel Approach,” Computers & Education 176 (2022), https://doi.org/10.1016/j.compedu.2021.104360 .
• “Information Literacy Standards for Teacher Education,” ACRL/EBSS, May 11, 2011, https://www.ala.org/acrl/sites/ala.org.acrl/files/content/standards/ilstandards_te.pdf .
• “Information Literacy Competency Standards for Higher Education,” ACRL, January 18, 2000, https://alair.ala.org/bitstream/handle/11213/7668/ACRL%20Information%20Literacy%20Competency%20Standards%20for%20Higher%20Education.pdf?sequence=1&isAllowed=y .
• “Connecting Justice to Frameworks: Information Literacy in Social Work,” YouTube video, 1:00:31, posted by ACRL, May 26, 2020, https://youtu.be/Re0pU6HJxEg .
• “Checklist for Developing and Reviewing Framework Companion Documents,” ACRL, revised February 2020, https://www.ala.org/acrl/resources/policies/checklist_ss_il .
• “Teaching the Teachers: A Collaborative Discussion on the Framework and Standards for Teacher Education Students,” ACRL/EBSS Instruction for Educators Committee, November 13, 2020, https://sites.google.com/view/ebss-ife-virtualdiscussion/home .
• “InTASC: Model Core Teaching Standards and Learning Progressions for Teachers 1.0,” Council of Chief State School Officers, accessed April 21, 2023, https://ccsso.org/sites/default/files/2017-12/2013_INTASC_Learning_Progressions_for_Teachers.pdf .
• “ISTE Standards: Educators,” International Society for Technology in Education, accessed April 21, 2023, https://www.iste.org/standards/iste-standards-for-teachers .
• “The ACRL Framework and Teacher Education: Shaping the Companion Document for Instruction for Education,” ACRL/EBSS Instruction for Educators Committee, accessed April 21, 2023, https://sites.google.com/view/ebssifedec2021workshop/home .
• “Companion Document to the ACRL Framework for Information Literacy for Higher Education: Instruction for Educators,” ACRL/EBSS/IFE, June 2023, https://acrl.libguides.com/ed .
• “Companion Document.”
• “Fulfilling the Framework: Strategies for Activating Information Literacy Skills for Pre-service Educators,” ACRL/EBSS Instruction for Educators Committee, December 9, 2022, https://sites.google.com/ewu.edu/ebssifefall22discussionsession/home .

Article Views (Last 12 Months)

Contact ACRL for article usage statistics from 2010-April 2017.

Article Views (By Year/Month)

© 2024 Association of College and Research Libraries , a division of the American Library Association

Print ISSN: 0099-0086 | Online ISSN: 2150-6698

ALA Privacy Policy

ISSN: 2150-6698

• Computer Vision
• Federated Learning
• Reinforcement Learning
• Natural Language Processing
• New Releases
• AI Dev Tools
• Advisory Board Members
• 🐝 Partnership and Promotion

The proposed framework’s effectiveness is underscored by its ability to recover constraints utilized in GDL, demonstrating its potential as a general-purpose framework for deep learning. GDL, which uses a group-theoretic perspective to describe neural layers, has shown promise across various applications by preserving symmetries. However, it encounters limitations when faced with complex data structures. The category theory-based approach overcomes these limitations and provides a structured methodology for implementing diverse neural network architectures.

The Centre of this research is applying category theory to understand and create neural network architectures. This approach enables the creation of neural networks that are more closely aligned with the structures of the data they process, enhancing both the efficiency and effectiveness of these models. The research highlights the universality and flexibility of category theory as a tool for neural network design, offering new insights into the integration of constraints and operations within neural network models.

In conclusion, this research introduces a groundbreaking framework based on category theory for designing neural network architectures. By bridging the gap between the specification of constraints and their implementations, the framework offers a comprehensive approach to neural network design. The application of category theory not only recovers and extends the constraints used in frameworks like GDL but also opens up new avenues for developing sophisticated neural network architectures. 

Check out the  Paper .  All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on  Twitter . Join our  Telegram Channel ,   Discord Channel , and  LinkedIn Gr oup .

If you like our work, you will love our  newsletter..

Don’t Forget to join our  39k+ ML SubReddit

Sana Hassan

Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.

• Sana Hassan https://www.marktechpost.com/author/sana-hassan/ Poro 34B: A 34B Parameter AI Model Trained for 1T Tokens of Finnish, English, and Programming languages, Including 8B Tokens of Finnish-English Translation Pairs
• Sana Hassan https://www.marktechpost.com/author/sana-hassan/ Researchers at Google AI Innovates Privacy-Preserving Cascade Systems for Enhanced Machine Learning Model Performance
• Sana Hassan https://www.marktechpost.com/author/sana-hassan/ Meet ChemBench: A Machine Learning Framework Designed to Rigorously Evaluate the Chemical Knowledge and Reasoning Abilities of LLMs
• Sana Hassan https://www.marktechpost.com/author/sana-hassan/ DRAGIN: A Novel Machine Learning Framework for Dynamic Retrieval Augmentation in Large Language Models and Outperforming Conventional Methods

RELATED ARTICLES MORE FROM AUTHOR

Google ai researchers propose a noise-aware training method (nat) for layout-aware language models, effector: a python-based machine learning library dedicated to regional feature effects, aurora-m: a 15b parameter multilingual open-source ai model trained in english, finnish, hindi, japanese, vietnamese, and code, silofuse: transforming synthetic data generation in distributed systems with enhanced privacy, efficiency, and data utility, api strategies for effective database management and integration, isobench: an artificial intelligence benchmark dataset containing problems from four major areas: math, science, algorithms, and games, aurora-m: a 15b parameter multilingual open-source ai model trained in english, finnish, hindi, japanese,..., silofuse: transforming synthetic data generation in distributed systems with enhanced privacy, efficiency, and data..., silo ai releases new viking model family (pre-release): an open-source llm for all nordic....

• AI Magazine
• Privacy & TC
• Cookie Policy

🐝 FREE AI Courses on RAG + Deployment of an Healthcare AI App + LangChain Colab Notebook all included

Thank You 🙌

Privacy Overview