Examining and Contextualizing Approaches to Establish Policy Support Organizations – A Mixed Method Study
Bibliographic record
Abstract
Background There has been an increase in the number of policy support organizations (PSOs) that have been created to foster the systematic use of evidence in health system policymaking. Our aim was to identify approaches for establishing a PSO or similar entities by soliciting insights from those with practical experience with developing and operationalizing PSOs in real-world contexts. Methods We used a sequential mixed method approached. We first conducted a survey to identify the views and experiences of those who were directly involved in the establishment of PSOs that have been developed and implemented across a variety of political-, health- and research-system contexts. The survey findings were then used to develop a purposive sample of PSO leaders and refine an interview guide for interviews with them. Results We received 19 completed surveys from leaders of PSOs in countries across the WHO regions and that operate in different settings (eg, as independent organization or within a university or government department) and conducted interviews with 15 senior managers from nine PSOs. Our findings provide in-depth insights about approaches and strategies across four stages for establishing a PSO, which include: (i) building awareness for the PSO; (ii) developing the PSO; (iii) assessing the PSO to identify potential areas for enhancement; and (iv) supporting maturation to build sustainability in the long-term. Our findings provide rich insights about the process of establishing a PSO from leaders who have undertaken the process. Conclusion While all PSOs share the same objective in supporting evidence-informed policy-making (EIPM), there is no single approach that can be considered to be the most successful in establishing a PSO, and each country should identify the approach based on its context.
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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 | 0.122 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".