Family Medicine in Iran: Facing the Health System Challenges
Bibliographic record
Abstract
BACKGROUND: In response to the current fragmented context of health systems, it is essential to support the revitalization of primary health care in order to provide a stronger sense of direction and integrity. Around the world, family medicine recognized as a core discipline for strengthening primary health care setting. OBJECTIVE: This study aimed to understand the perspectives of policy makers and decision makers of Iran's health system about the implementation of family medicine in Iran urban areas. MATERIALS/PATIENTS & METHODS: This study is a qualitative study with framework analysis. Purposive semi-structured interviews were conducted with Policy and decision makers in the five main organizations of Iran health care system. The codes were extracted using inductive and deductive methods. RESULTS: According to 27 semi-structured interviews were conducted with Policy and decision makers, three main themes and 8 subthemes extracted, including: The development of referral system, better access to health care and the management of chronic diseases. CONCLUSION: Family medicine is a viable means for a series of crucial reforms in the face of the current challenges of health system. Implementation of family medicine can strengthen the PHC model in Iran urban areas. Attempting to create a general consensus among various stakeholders is essential for effective implementation of the project.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".