A multi-faceted approach to promote knowledge translation platforms in eastern Mediterranean countries: climate for evidence-informed policy
Bibliographic record
Abstract
OBJECTIVES: Limited work has been done to promote knowledge translation (KT) in the Eastern Mediterranean Region (EMR). The objectives of this study are to: 1.assess the climate for evidence use in policy; 2.explore views and practices about current processes and weaknesses of health policymaking; 3.identify priorities including short-term requirements for policy briefs; and 4.identify country-specific requirements for establishing KT platforms. METHODS: Senior policymakers, stakeholders and researchers from Algeria, Bahrain, Egypt, Iran, Jordan, Lebanon, Oman, Sudan, Syria, Tunisia, and Yemen participated in this study. Questionnaires were used to assess the climate for use of evidence and identify windows of opportunity and requirements for policy briefs and for establishing KT platforms. Current processes and weaknesses of policymaking were appraised using case study scenarios. Closed-ended questions were analyzed descriptively. Qualitative data was analyzed using thematic analysis. RESULTS: KT activities were not frequently undertaken by policymakers and researchers in EMR countries, research evidence about high priority policy issues was rarely made available, and interaction between policymakers and researchers was limited, and policymakers rarely identified or created places for utilizing research evidence in decision-making processes. Findings emphasized the complexity of policymaking. Donors, political regimes, economic goals and outdated laws were identified as key drivers. Lack of policymakers' abilities to think strategically, constant need to make quick decisions, limited financial resources, and lack of competent and trained human resources were suggested as main weaknesses. CONCLUSION: Despite the complexity of policymaking processes in countries from this region, the absence of a structured process for decision making, and the limited engagement of policymakers and researchers in KT activities, there are windows of opportunity for moving towards more evidence informed 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 | 0.229 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".