A comparative study of primary health care management in selected countries and designing a model for Iran
Bibliographic record
Abstract
Abstract BACKGROUND: In this research Primary Health Care systems were reviewed and the nurses' roles were determined and then a model was designed for health networks in Iran. METHODS: This was a triangulation research done in comparative method. In first step, PHC systems reviewed in different countries such as UK, Australia, Canada, Sweden and Turkey selected in purposive sampling. In second step, the process of management of PHC services in selected countries were determined from accessibility, providers and referral system, and then compared to PHC system in Iran. Afterward a primary model was designed. In third step, the model was validated using experts judgment (n = 30) and the results analyzed by descriptive statistics and final model was designed. RESULTS: In all of the studied countries, PHC services were delivered by health team including family physicians, nurses, midwives, and health technicians in systematic network including local health centers, family physicians offices and nursing clinics. Family physicians and nurses had a basic role in delivery of services. Also other health practitioners such as psychiatrists were practiced with health team. PHC services in most cases on the bases of people's need and health information were transmitted between the providers by health files. The effective referral system exists between health services. CONCLUSION: The model of PHC delivery was on the bases of health team with systematic network of the local health centers and provides accessibility, quality and comprehensively of services. We suggest to employee educated nurses in health centers to provide more health services. KEY WORDS: Primary health care, Management, Health care service providing system, Iran.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".