Examining and Contextualizing Approaches to Establish Policy Support Organizations – A Critical Interpretive Synthesis
Bibliographic record
Abstract
BACKGROUND: In response to worldwide calls for the need to support evidence-informed policy-making (EIPM), more countries are increasingly interested in enhancing their efforts to use research to inform policy-making. In order to inform the efforts of those asked to lead the support of EIPM, our aim is to develop a conceptual framework to guide the process of establishing a policy support organization (PSO). METHODS: We conducted a critical interpretive synthesis (CIS). We conducted a two steps literature review. In the second step, we systematically searched OVID EMBASE, PsychInfo, HealthStar, CINAHL, Web of Science, Social Science Abstract, Health Systems Evidence, and ProQuest Dissertations and Theses Global databases for documents reporting the establishment of PSOs and the contextual factors influencing the process of establishing these organizations. We assessed the eligibility of the retrieved articles and synthesized the findings iteratively. RESULTS: We included 52 documents in the synthesis. Our findings suggest that a PSO establishment process has four interconnected stages: awareness, development, assessment, and maturation. The process of establishing a PSO is iterative and influenced by political, research and health systems contextual factors, which determine the availability of the resources and the trust between researchers and policy-makers. The contextual factors have an impact on each other, and the challenges that arise from one factor can be mitigated by other factors. CONCLUSION: For those interested in establishing a PSO, our framework provides a road map for identifying the most appropriate starting point and the factors that might influence the establishment process. Leaders of such PSOs can use our findings to expand or refine their scope of work. Given that this framework focuses only on PSOs in the health sector, an important next step for research would be to include other sectors from social systems and identify any additional insight that can enhance our framework.
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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 | 0.402 | 0.512 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.069 | 0.038 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.033 | 0.035 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.008 | 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; 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".