Desirable attributes of theories, models, and frameworks for implementation strategy design in healthcare: a scoping review protocol
Bibliographic record
Abstract
Background: Implementation strategies can facilitate the adoption of evidence-based practices and policies. A wide range of theoretical approaches—theories, models, and frameworks—can be used to inform implementation strategy design in different ways (e.g., guiding barrier and enabler assessment to implementing evidence-based interventions). While selection criteria and attributes of theoretical approaches for use in implementation strategy design have been studied, they have never been synthesized. Furthermore, theoretical approaches have never been classified according to desirable criteria and attributes for use in implementation strategy design. This scoping review aims to a) identify the literature reporting on the selection of theoretical approaches for informing implementation strategy design in healthcare and b) understand the suggested use of these approaches in implementation strategy design. Methods: The Joanna Briggs Institute methodological guidelines will be used to conduct this scoping review. A search of three bibliographical databases (MEDLINE, Embase, CINAHL) will be conducted for peer-reviewed discussion, methods, protocol, or review papers. Data will be managed using the Covidence software. Two review team members will independently perform screening, full text review and data extraction. Results: Results will include a list of selection criteria and attributes of theoretical approaches for use in research on implementation strategy design. Descriptive data regarding selection criteria and attributes will be synthesized graphically and in table format. Data regarding the suggested use of theoretical approaches in implementation strategy design will be presented narratively. Conclusions: Results will be used to classify existing theoretical approaches according to the attributes and selection criteria identified in this scoping review. Envisioned next steps include an online tool that will be created to assist researchers in selecting theories, models, and frameworks for implementation strategy design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.358 | 0.361 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.075 | 0.022 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".