What factors affect evidence-informed policymaking in public health? Protocol for a systematic review of qualitative evidence using thematic synthesis
Bibliographic record
Abstract
BACKGROUND: Claims of and calls for evidence-informed policymaking pervade public health journals and the literature of governments and global health agencies, yet our knowledge of the arrangements most conducive to the appropriate use of evidence is incomplete and fragmented. Designing interventions to encourage evidence use by policymakers requires an understanding of the processes through which officials access, assess and use research, including technical and political factors related to evidence uptake, and the ways in which the policymaking context can affect these processes. This review aims to systematically locate, synthesise and interpret the existing qualitative work on the process of evidence use in public health policymaking, with the aim of producing an empirically derived taxonomy of factors affecting evidence use. METHODS/DESIGN: This review will include primary qualitative studies that examined the use of research evidence by policymakers to inform decisions about public health. To locate studies, we will search nine bibliographic databases, hand-search nine public health and policy journals and scan the websites of relevant organisations and the reference lists of previous reviews of evidence use in policymaking. Two reviewers will independently screen studies, apply inclusion criteria and appraise the quality of included studies. Data will be coded inductively and analysed using thematic synthesis. An augmented version of the CASP Qualitative Checklist will be used to appraise included studies, and the CERQual tool will be used to assess confidence in the review's findings. The review's results will be presented narratively and in tabular form. Synthesis findings will be summarised as a taxonomy of factors affecting evidence use in public health policymaking. A conceptual framework explaining the relationships between key factors will be proposed. Implications and recommendations for policy, practice and future research will be discussed. DISCUSSION: This review will be the most comprehensive to date to synthesise the qualitative literature on evidence use by public health policymakers and will be the first to apply a formal method of qualitative metasynthesis to this body of evidence. Its results will be useful both to scholars of evidence use and knowledge translation and to decision-makers and academics attempting to influence public health policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.178 | 0.509 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.029 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".