Health Priority Setting in Iran: Evaluating Against the Social Values Framework
Bibliographic record
Abstract
BACKGROUND: Health systems, as part of the social system, consider public values. This study was conducted to examine the role of social values in the health priority setting in the Iranian health system. METHODS: In this qualitative case study, three main data sources were used: literature, national documents, and key informants who were purposefully selected from health care organizations and other related institutions. Data was analyzed and interpreted using the Clark-Weale Framework. RESULTS: According to our results, the public indirectly participates in decision-making. The public representatives participate in the meetings of the health priority setting as parliament members, representatives of some unions, members of the city council, and donors. The transparency of the decisions and the accountability of the decision makers are low. Decision makers only respond to complaints of the Audit Court and the Inspection Organization. Individual choice, although respected in hospitals and clinics, is limited in health care networks because of the referral system. Clinical effectiveness is considered in insurance companies and some hospitals. There are no technical abilities to determine the cost-effectiveness of health technologies; however, some international experiences are employed. Equity and solidarity are considered in different levels of the health system. CONCLUSION: Social values are considered in the health priority decisions in limited ways. It seems that the lack of an appropriate value-based framework for priority setting and also the lack of public participation are the major defects of the health system. It is recommended that health policymakers invite different groups of people and stakeholders for active involvement in health priority decisions.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".