Curative Care Utilization under Family Medicine and Rural Insurance in Amol – Iran
Bibliographic record
Abstract
Background and purpose: Reliable information about utilization of medical services is key for making appropriate decisions of all healthcare systems. Nonetheless, most policy decisions and planning in the rural areas of developing countries are made with the lack of such crucial information. In this article we attempt to reveal the pattern of curative care utilization of rural population in Amol, a county in Northern Province of Mazandaran. Methods: In this study 355 patients living in rural area who in the last three month utilized curative care from different providers were interviewed in their doorsteps. All interviewees were heads of family or people age above 15. SPSS software was used for analyzing the data. Results: About a quarter of patients (24.5%) have referred to their local family physicians. It is noticeable that the proportion of people who referred to GP out of family physicians scheme exceeds the proportion of patients referred to GPs who are working as family physicians in the FMRI scheme. Among the studied variables, only basic insurance, severity of disease, and type of care utilized had significant association with referred or not referred of individuals to their own family physicians.Conclusion:Family medicine and rural insurance in Iran has increased the overall service utilization of population in rural areas but not in the scale that the government has spent its limited healthcare resources. This raises the concern of inappropriate resource allocation for inappropriate people and inappropriate services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".