Resources

Meza, R. D., Moreland, J. C., Pullmann, M. D., Klasnja, P., Lewis, C. C., & Weiner, B. J. (2023). Theorizing is for everybody: Advancing the process of theorizing in implementation science. Frontiers in health services, 3. DOI: 10.3389/frhs.2023.1134931. PMID: 36926499

This paper provide recommendations for advancing the process of theorizing in implementation science to stimulate the use of developing and advancing theory.

Mielke, J., Brunkert, T., Zúñiga, F., Simon, M., Zullig, L. L., & De Geest, S. (2022). Methodological approaches to study context in intervention implementation studies: an evidence gap map. BMC medical research methodology, 22(1), 1-19. DOI: 10.1186/s12874-022-01772-w. PMID: 36517765.

This study systematically reviewed and mapped current methods to contextualize analysis in intervention implementation studies.

 

Gama, F., Tyskbo, D., Nygren, J., Barlow, J., Reed, J., & Svedberg, P. (2022). Implementation frameworks for artificial intelligence translation into health care practice: scoping review. Journal of medical internet research, 24(1). DOI: 10.2196/32215. PMID: 35084349

This paper identifies implementation frameworks to understand the use of Artificial Intelligence (AI) in healthcare settings.

 

Tucker, S., McNett, M., Mazurek Melnyk, B., Hanrahan, K., Hunter, S. C., Kim, B., … & Kitson, A. (2021). Implementation science: Application of evidence‐based practice models to improve healthcare quality. Worldviews on evidence‐based nursing, 18(2), 76-84. DOI: 10.1111/wvn.12495. PMID: 33779042.

Translating research into practice can be complicated. This paper provides information around implementation science and models to facilitate the implementation of Evidence-Based Practices in local healthcare settings.

 

Walsh-Bailey, C., Tsai, E., Tabak, R.G. et al. A scoping review of de-implementation frameworks and models. Implementation science 16, 100 (2021). DOI: 10.1186/s13012-021-01173-5. PMID: 34819122.

Reducing or eliminating healthcare services that are inappropriate, ineffective, or potentially harmful can help ensure limited resources are being properly utilized. This paper conducted a review of de-implementation studies and organized them by setting, study design, methodology, and frameworks and model (FM) characteristics.

Pinto, R. M., Park, S. (Ethan), Miles, R., & Ong, P. N. (2021). Community engagement in dissemination and implementation models: A narrative review. Implementation research and practice, 2. DOI:10.1177/2633489520985305.

Community engagement remains a pillar of D&I research. This article reviews, in narrative form, the evidence-based models that focus on community engagement and highlights that very few of these models have been created since 2009.

Moullin, J.C., Dickson, K.S., Stadnick, N.A. et al. Ten recommendations for using implementation frameworks in research and practice. Implementation science communications 1, 42 (2020). DOI:10.1186/s43058-020-00023-7. PMID: 32885199.

Evidence supports the use of implementation science (IS) models when conducting IS research although, these models are not used as often as they could be. This paper makes recommendations for the application of IS models for researchers and practitioners.

Smith, J.D., Li, D.H. & Rafferty, M.R. The Implementation Research Logic Model: a method for planning, executing, reporting, and synthesizing implementation projects. Implementation science 15, 84 (2020). DOI: 10.1186/s13012-020-01041-8. PMID: 32988389.

This paper introduces The Implementation Research Logic Model (IRLM) to help strengthen the connection and rationale for using particular models to fit specific research questions. It aims to both increase the scientific rigor and the transparency of model application and connection of core elements in an implementation science research project.

 

Nilsen, P., Ingvarsson, S., Hasson, H., von Thiele Schwarz, U., & Augustsson, H. (2020). Theories, models, and frameworks for de-implementation of low-value care: A scoping review of the literature. Implementation research and practice, 1. DOI: 10.1177/2633489520953762.

This paper provided a review of the literature around identifying theories, models, and frameworks (TMFs) for understanding the processes and determinants of to de-implementing low-value care.

 

Esmail, R., Hanson, H., Holroyd-Leduc, J. et al. A scoping review of full-spectrum knowledge translation theories, models, and frameworks. Implementation science, 15, 11 (2020). DOI: 10.1186/s13012-020-0964-5. PMID: 32059738.

Knowledge Translation (KT) is a way to incorporate evidence into clinical care. This study identifies and describes KT TMFs to provide guidance to users.

 

Damschroder, L. J. (2020). Clarity out of chaos: use of theory in implementation research. Psychiatry research, 283, 112461. DOI: 10.1016/j.psychres.2019.06.036.  PMID: 31257020

This article provides an overview of the type and roles of theory in Implementation Science (IS), resources for selecting appropriate IS frameworks, and examples of these frameworks.

Nilsen, P., & Bernhardsson, S. (2019). Context matters in implementation science: a scoping review of determinant frameworks that describe contextual determinants for implementation outcomes. BMC health services research, 19(1), 189. DOI: 10.1186/s12913-019-4015-3. PMID: 30909897

The relevance of context in implementation science is reflected in the numerous theories, frameworks, models and taxonomies that have been proposed to analyse determinants of implementation (in this paper referred to as determinant frameworks). This scoping review aimed to investigate and map how determinant frameworks used in implementation science were developed, what terms are used for contextual determinants for implementation, how the context is conceptualized, and which context dimensions that can be discerned.

 

Kislov, R., Pope, C., Martin, G. P., & Wilson, P. M. (2019). Harnessing the power of theorising in implementation science. Implementation science, 14(1), 103. DOI: 10.1186/s13012-019-0957-4. PMID: 31823787

This article discusses the benefit of shifting perspective of theories being a product, and instead how they can guide the process of empirical research to be bi-directional to advance knowledge.

This paper provides the results of a scoping review of knowledge translation (KT) theories, models, and frameworks that have been used to guide dissemination or implementation of evidence-based interventions targeted to prevention and/or management of cancer or other chronic diseases.

This paper describes initial criteria for the selection of implementation theories based on an investigation of which theories implementation scientists use, how they use theories, and the criteria used to select theories.

Tabak, R. G., Khoong, E. C., Chambers, D. A., & Brownson, R. C. (2012). Bridging research and practice: models for dissemination and implementation research. American journal of preventive medicine, 43(3), 337–350. DOI: 10.1016/j.amepre.2012.05.024. PMID: 22898128

Theories and frameworks (hereafter called models) enhance dissemination and implementation (D&I) research by making the spread of evidence-based interventions more likely. This work organizes and synthesizes these models by (1) developing an inventory of models used in D&I research; (2) synthesizing this information; and (3) providing guidance on how to select a model to inform study design and execution.

Wilson, P. M., Petticrew, M., Calnan, M. W., & Nazareth, I. (2010). Disseminating research findings: what should researchers do? A systematic scoping review of conceptual frameworks. Implementation science, 5, 91. DOI: 10.1186/1748-5908-5-91. PMID: 21092164

Addressing deficiencies in the dissemination and transfer of research-based knowledge into routine clinical practice is high on the policy agenda both in the UK and internationally. However, there is lack of clarity between funding agencies as to what represents dissemination. Moreover, the expectations and guidance provided to researchers vary from one agency to another. This paper is based on a systematic scoping review of any conceptual/organizing frameworks that could be used by researchers to guide their dissemination activity.

Mitchell, S. A., Fisher, C. A., Hastings, C. E., Silverman, L. B., & Wallen, G. R. (2010). A thematic analysis of theoretical models for translational science in nursing: mapping the field. Nursing outlook, 58(6), 287–300. DOI: 10.1016/j.outlook.2010.07.001. PMID: 21074646.

The quantity and diversity of conceptual models in translational science may complicate rather than advance the use of theory. This paper offers a comparative thematic analysis of the models available to inform knowledge development, transfer, and utilization. This analysis distinguishes the contributions made by leaders and researchers at each phase in the process of discovery, development, and service delivery. It also informs the selection of models to guide activities in knowledge translation.