Barriers, facilitators, strategies and outcomes to engaging policymakers, healthcare managers and policy analysts in knowledge synthesis: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Engaging policymakers, healthcare managers and policy analysts in the conduct of knowledge synthesis can help increase its impact. This is particularly important for knowledge synthesis studies commissioned by decision-makers with limited timelines, as well as reviews of health policy and systems research. A scoping review will be conducted to assess barriers, facilitators, strategies and outcomes of engaging these individuals in the knowledge synthesis process. METHODS AND ANALYSIS: We will follow the Joanna Briggs Institute guidance for scoping reviews. Literature searches of electronic databases (eg, MEDLINE, EMBASE, Cochrane Library, ERIC, PsycINFO) will be conducted from inception onwards. The electronic search will be supplemented by searching for sources that index unpublished/difficult to locate studies (eg, GreyNet International database), as well as through scanning of reference lists of reviews on related topics. All study designs using either qualitative or quantitative methodologies will be eligible if there is a description of the strategies, barriers or facilitators, and outcomes of engaging policymakers, healthcare managers and policy analysts in the knowledge synthesis process. Screening and data abstraction will be conducted by 2 team members independently after a calibration exercise across the team. A third team member will resolve all discrepancies. We will conduct frequency analysis and thematic analysis to chart and characterise the literature, identifying data gaps and opportunities for future research, as well as implications for policy. ETHICS AND DISSEMINATION: This project was commissioned by the Alliance for Health Policy and Systems Research, WHO. The results will be used by Alliance Review Centers of health policy and systems research in low-income and middle-income countries that are conducting knowledge synthesis to inform health policymaking and decision-making. Our results will also be disseminated through conference presentations, train-the-trainer events, peer-reviewed publication and a 1-page policy brief that will be posted on the authors' websites.
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 | 0.284 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.074 | 0.033 |
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".