Use of health systems and policy research evidence in the health policymaking in eastern Mediterranean countries: views and practices of researchers
Bibliographic record
Abstract
BACKGROUND: Limited research exists on researchers' knowledge transfer and exchange (KTE) in the eastern Mediterranean region (EMR). This multi-country study explores researchers' views and experiences regarding the role of health systems and policy research evidence in health policymaking in the EMR, including the factors that influence health policymaking, barriers and facilitators to the use of evidence, and the factors that increase researchers' engagement in KTE. METHODS: Researchers who published health systems and policy relevant research in 12 countries in the EMR (Bahrain, Egypt, Iran, Jordan, Lebanon, Libya, Morocco, Oman, Palestine, Sudan, Syria, and Yemen) were surveyed. Descriptive analysis and Linear Mixed Regression Models were performed for quantitative sections and the simple thematic analysis approach was used for open-ended questions. RESULTS: A total of 238 researchers were asked to complete the survey (response rate 56%). Researchers indicated transferring results to other researchers (67.2%) and policymakers in the government (40.5%). Less than one-quarter stated that they produced policy briefs (14.5%), disseminated messages that specified possible actions (24.4%), interacted with policymakers and stakeholders in priority-setting (16%), and involved them in their research (19.8%). Insufficient policy dialogue opportunities and collaboration between researchers and policymakers and stakeholders (67.9%), practical constraints to implementation (66%), non-receptive policy environment (61.3%), and politically sensitive findings (57.7%) hindered the use of evidence. Factors that increase researchers' engagement in KTE activities in the region were associated with involving policymakers and stakeholders at various stages such as priority-setting exercises and provision of technical assistance. CONCLUSIONS: Researchers in the EMR recognize the importance of using health systems evidence in health policymaking. Potential strategies to improve the use of research evidence emphasize two-way communication between researchers and policymakers. Findings are critical for the upcoming World Health Report 2012, which will emphasize the significance of conducting and translating health research to inform health policies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | MetaresearchScholarly communication Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | MetaresearchScholarly communication Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | MetaresearchScholarly communication Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.270 | 0.276 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".