How Contexts and Issues Influence the Use of Policy‐Relevant Research Syntheses: A Critical Interpretive Synthesis
Bibliographic record
Abstract
CONTEXT: Evidence briefs have emerged as a promising approach to synthesizing the best available research evidence for health system policymakers and stakeholders. An evidence brief may draw on systematic reviews and many other types of policy-relevant information, including local data and studies, to describe a problem, options for addressing it, and key implementation considerations. We conducted a systematic review to examine the ways in which context- and issue-related factors influence the perceived usefulness of evidence briefs among their intended users. METHODS: We used a critical interpretive synthesis approach to review both empirical and nonempirical literature and to develop a model that explains how context and issues influence policymakers' and stakeholders' views of the utility of evidence briefs prepared for priority policy issues. We used a "compass" question to create a detailed search strategy and conducted electronic searches in CINAHL, EMBASE, HealthSTAR, IPSA, MEDLINE, OAIster (gray literature), ProQuest A&I Theses, ProQuest (Sociological Abstracts, Applied Social Sciences Index and Abstracts, Worldwide Political Science Abstracts, International Bibliography of Social Sciences, PAIS, Political Science), PsychInfo, Web of Science, and WilsonWeb (Social Science Abstracts). Finally, we used a grounded and interpretive analytic approach to synthesize the results. FINDINGS: Of the 4,461 papers retrieved, 3,908 were excluded and 553 were assessed for "relevance," with 137 included in the initial sample of papers to be analyzed and an additional 23 purposively sampled to fill conceptual gaps. Several themes emerged: (1) many established types of "evidence" are viewed as useful content in an evidence brief, along with several promising formatting features; (2) contextual factors, particularly the institutions, interests, and values of a given context, can influence views of evidence briefs; (3) whether an issue is polarizing and whether it is salient (or not) and familiar (or not) to actors in the policy arena can influence views of evidence briefs prepared for that issue; (4) influential factors can emerge in several ways (as context driven, issue driven, or a result of issue-context resonance); (5) these factors work through two primary pathways, affecting either the users or the producers of briefs; and (6) these factors influence views of evidence briefs through a variety of mechanisms. CONCLUSIONS: Those persons funding and preparing evidence briefs need to consider a variety of context- and issue-related factors when deciding how to make them most useful in policymaking.
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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 | 0.699 | 0.873 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.066 | 0.042 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.040 | 0.038 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".