Effect of the Implementation of the Family Physician Program 2015 on Fair Accessibility for People to Health Care Services in the Sistan Region
Bibliographic record
Abstract
Equitable access to primary health care is an indispensable right and a basic need of all human beings. Currently, the development of any society is judged based on the level of public access to primary health care services. This comparative study attempted to examine the fairness accessibility of people in Sistan to health care services through Family Physician Program 2015. This was a descriptive, analytical research focusing on the level of equitable public access to primary health care in Sistan. Samples were taken from all the service-providing centers. Data were collected through HNIS software, network management center to analyze the gathered data. The results showed that prior to the implementation of the family doctor plan (before 2005), there was a doctor for every 9545 people, a midwife for every 10,000 people and one paramedic for 1,111 people. After beginning the family doctor plan, the figures showed that there was one doctor or MD for every 3387 people and one midwife for every 2916 people, and one health worker for every 549 rural residents. The implementation of the family physician program was an opportunity for the health system in Sistan region, where the appropriate resources management and equitable distribution of health care services throughout the region could facilitate accessibility to identical 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".