Evolution of Iran’s health research system over the past 50 years: a narrative review
Bibliographic record
Abstract
BACKGROUND: A substantial growth has been reported in Iran's health research output over the last recent decades, throughout the times of economic, social, and political instability. This study reviewed the existing literature to provide a better understanding of the evolution of Iran's health research system over this period. METHODS: A narrative review of studies addressing health research system (HRS) in Iran was performed. The search strategy and categorization of the retrieved data was informed by the HRS framework of the World Health Organization (WHO). This framework proposes four functions for HRS: (i) stewardship; (ii) financing; (iii) creating and sustaining resources; and (iv) producing and using research. Searches in MEDLINE through PubMed (using MeSH terms) complemented with semantic searches through PubMed and Google Scholar were conducted. RESULTS: After removing the duplicates, 805 articles were retrieved, of which 601 were irrelevant, and 204 were reviewed. CONCLUSIONS: Iran has made substantial progress in different components of its HRS over the last few decades, such as starting a discourse surrounding health research ethics, priority-setting, and placing monitoring mechanisms while increasing the capacity for conducting and publishing research. However, there is still room for improvements, or even a need for fundamental changes, in several components, such as regarding increasing the research budget and improving the funding allocation mechanisms; improving the education curriculum; and promoting the use of evidence. The findings emphasized that improvement of HRS functions requires addressing context-specific problems. This review provides essential lessons to share with other low- and middle-income countries and international organizations, eg, the WHO.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".