Analysis and evolution of health policies in Iran through policy triangle framework during the last thirty years: a systematic review of the historical period from 1994 to 2021.
Bibliographic record
Abstract
Background: Health policy analysis as a multi-disciplinary approach to public policy illustrates the need for interventions that highlight and address important policy issues, improve the policy formulation and implementation process and lead to better health outcomes. Various theories and frameworks have been contributed as the foundation for the analysis of policy in various studies. This study aimed to analyze health policies during the historical period of the almost last 30 years in Iran using policy triangle framework. Method: To conduct the systematic review international databases (PubMed / Medline, Scopus, Web of Sciences, CINAHL, PsycINFO, Embase, the Cochran Library) and Iranian databases from January 1994 to January 2021 using relevant keywords. A thematic qualitative analysis approach was used for the synthesis and analysis of data. The Critical Appraisal Skills Programme for Qualitative Studies Checklist (CASP) was conducted. Results: Out of 731 articles, 25 articles were selected and analyzed. Studies used health policy triangle framework to analyze policies in the Iranian health sector has been published since 2014. All the included studies were retrospective. The main focus of most of studies for the analysis was on the context and process of polices as the elements of the policy triangle. Conclusion: The main focus of health policy analysis studies in Iran over the last thirty years was on the context and process of polices. Although range of actors within and outside the Iran government influence health policies but in many policy processes the power and the role of all actors or players involved in the policy are not recognized carefully. Also, Iran's health sector suffers from lack of a proper framework for evaluating various implemented policies.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".