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Characterizing evolving frameworks: issues from Esmail et al. (2020) review
Russell E Glasgow1,2, Paul A Estabrooks3, Marcia G Ory4,5,6
1Family Medicine, University of Colorado School of Medicine, 13199 E. Montview Blvd, Aurora, CO, 80045, USA. russell.glasgow@cuanschutz.edu.
Implementation Science : IS
|July 3, 2020
Summary
This study highlights mischaracterizations of the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework in a recent review. It discusses overlooking framework evolution and suggests improvements for future reviews in implementation science.
Area of Science:
- Implementation Science
- Knowledge Synthesis
- Framework Analysis
Background:
- Understanding and categorizing implementation science theories, models, and frameworks presents complex challenges.
- Systematic reviews are crucial for synthesizing knowledge but are complicated by numerous evolving frameworks.
- Accurate representation of frameworks is vital for their effective application and advancement.
Discussion:
- Advances and extensions to frameworks beyond their original publication or influential reviews are frequently overlooked.
- Inadvertent mischaracterization of a framework's scope and application can lead to significant negative consequences.
- The paper discusses the broader issue of how evolving frameworks are treated in scientific literature.
Key Insights:
- The RE-AIM framework was mischaracterized in a recent review, indicating a broader problem with how evolving implementation science tools are understood.
- Accurate representation of framework capabilities and limitations is essential for effective application and research synthesis.
- Failure to acknowledge framework evolution can impede scientific progress and the successful translation of research into practice.
Outlook:
- Proposes solutions for reviewers, framework developers, and scholars to prevent or mitigate mischaracterization issues.
- Encourages greater attention to the dynamic nature of implementation science frameworks.
- Aims to improve the accurate use and understanding of established and evolving implementation science models.