A roadmap for strengthening evidence-informed health policy-making in Iran: protocol for a research programme
Bibliographic record
Abstract
BACKGROUND: Many initiatives have been taken in the Islamic Republic of Iran to promote evidence-informed health policy-making (EIHP). However, these initiatives are not systematic. Since the implementation of EIHP is not consistent and the interventions in this regard are complex, a comprehensive plan could be a useful tool for employing initiatives to achieve and promote EIHP. Hence, this study aims to develop a roadmap for strengthening EIHP over a 3-year period in Iran. METHODS: Nine projects will be conducted to define the roadmap for strengthening EIHP. These projects include two reviews and a stakeholder analysis to identify the factors that facilitate or hinder achieving EIHP. The next study will be a qualitative study to prioritise the challenges and outline the main causes. The following steps will be a review of reviews to extract global experiences on interventions used for strengthening EIHP and two qualitative studies to examine the adoption of these interventions and develop an operational plan for strengthening EIHP in Iran. The research will be completed through conducting two qualitative-quantitative studies to design a tool for measuring EIHP and assessing EIHP in Iran at baseline. DISCUSSION: This national EIHP roadmap will surely be able to identify the gaps and bumps that might exist in the implementation plan for establishing EIHP and eliminate them as needed in the future. This roadmap can be a step in moving towards transparency and accountability in the health system and as thus towards good governance and improvement of the health system's performance. Although the plan can be a good model for developing countries and may promote the use of evidence in health policy-making, we should assume that there are some critical contextual factors that could potentially hinder the complete and successful implementation of EIHP. Thus, to enhance EIHP in these countries with a policy-making context that does not fully support the use of evidence, it is crucial to think about not only those interventions that directly address the EIHP barriers, but also some long-term strategies to make required changes in the context, both beyond and within the health system.
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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 |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.133 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".