A Conceptual Framework for Evaluation of Public Health and Primary Care System Performance in Iran
Bibliographic record
Abstract
INTRODUCTION: The main objective of this study was to design a conceptual framework, according to the policies and priorities of the ministry of health to evaluate provincial public health and primary care performance and to assess their share in the overall health impacts of the community. METHODS: We used several tools and techniques, including system thinking, literature review to identify relevant attributes of health system performance framework and interview with the key stakeholders. The PubMed, Scopus, web of science, Google Scholar and two specialized databases of Persian language literature (IranMedex and SID) were searched using main terms and keywords. Following decision-making and collective agreement among the different stakeholders, 51 core indicators were chosen from among 602 obtained indicators in a four stage process, for monitoring and evaluation of Health Deputies. RESULTS: We proposed a conceptual framework by identifying the performance area for Health Deputies between other determinants of health, as well as introducing a chain of results, for performance, consisting of Input, Process, Output and Outcome indicators. We also proposed 5 dimensions for measuring the performance of Health Deputies, consisting of efficiency, effectiveness, equity, access and improvement of health status. CONCLUSION: The proposed Conceptual Framework illustrates clearly the Health Deputies success in achieving best results and consequences of health in the country. Having the relative commitment of the ministry of health and Health Deputies at the University of Medical Sciences is essential for full implementation of this framework and providing the annual performance report.
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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 | 0.038 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".