Advancing the selection of implementation science theories, models, and frameworks: a scoping review and the development of the SELECT-IT meta-framework
Bibliographic record
Abstract
BACKGROUND: Theories, models, and frameworks (TMFs) are central to implementation practice and research. Selecting one or more TMF(s) for a project remains challenging due to numerous options and limited guidance. This study aimed to (1) identify and categorize the reported purposes and attributes of TMFs, as well as the practical considerations of TMF users, and (2) synthesize these findings into a meta-framework that supports implementation practitioners and researchers in selecting TMFs. METHODS: A scoping review was conducted using Joanna Briggs Institute guidelines. Medline, Embase, and CINAHL were searched to identify articles on the selection of TMFs. Articles were selected and data extracted using Covidence. Inductive thematic analysis was used to refine and categorize purposes, attributes and practical considerations. The meta-framework was developed by mapping these categories onto a sequential process, pilot-testing through case studies, and iteratively refining it based on team feedback. RESULTS: Of 9,276 records, 43 articles (2005-2024) were included. Most articles reported TMF purposes (41 articles), followed by attributes (30) and practical considerations (13). Seven distinct purposes were identified: (1) enhancing conceptual clarity, (2) anticipating change and guiding inquiry, (3) guiding the implementation process, (4) guiding identification of determinants, (5) guiding design and adaptation of strategies, (6) guiding evaluation and causal explanation, and (7) guiding interpretation and dissemination. Additionally, 24 TMF attributes were grouped into five domains: clarity and structure, scientific strength and evidence, applicability and usability, equity and sociocultural responsiveness, and system and partner integration. Ten practical considerations were grouped into three domains: team expertise and readiness, resource availability, and project fit. These findings informed the development of the Systematic Evaluation and Selection of Implementation Science Theories, Models and Frameworks (SELECT-IT) meta-framework, comprising four steps: (1) determine the purpose(s) of using TMF(s); (2) identify potential TMFs; (3) evaluate short-listed TMFs against attributes; and (4) assess practical considerations of using TMF(s) within the project context. A worked example and two user-friendly worksheets illustrate its utility. CONCLUSIONS: This study advances understanding of the selection of implementation science TMFs by distinguishing inherent TMF attributes from practical considerations. The SELECT-IT meta-framework offers a structured, context-sensitive approach for selecting appropriate TMFs. Future research should evaluate its validity and utility across diverse contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.454 | 0.545 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.106 | 0.068 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".