Assessing Health Inequalities in Iran: A Focus on the Distribution of Health Care Facilities
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Equality in distribution of health care facilities is the main cause for access and enjoyment to the health. The aim of this study was to examine the regional disparities in health care facilities across the Markazi province. METHODS: This was a cross-sectional study. Study sample included the cities of Markazi province, ranked based on 15 health indices. Data was collected by a data collection form made by the researcher using statistical yearbook. The indices were weighted using Shannon entropy. Finally, technique for order preference by similarity to ideal solution (TOPSIS) was used to rank the towns of the province in terms of access to health care facilities. RESULTS: There is a large gap between cities of Markazi province in terms of access to health care facilities. Shannon entropy introduced the number of urban health centers per 1000 people as the most important indicator and the number of rural active health house per 1000 people as the less important indicator. According to TOPSIS, the towns of Ashtian and Shazand ranked the first and last (10th) respectively in access to health services. CONCLUSION: There are significant inequalities in distribution of health care facilities in Markazi province. We propose that policy makers determine resource allocation priorities according to the degree of development for a balanced and equal distribution of health care facilities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".