Geographic Distribution of Active Medical Specialists in Iran: A Three-Source Capture-Recapture Analysis.
Bibliographic record
Abstract
BACKGROUND: Estimation of health workforce supply becomes problematic when there is no knowledge about the number of active specialists. The aim of this study is to estimate active specialists and their geographic accessibility in Iran. METHODS: We enrolled all medical specialists from the Iranian Ministry of Health database (14151), national hospitals survey (28898) and Continuing Medical Education registries (13159) in 2015. Duplicate records across the three registries were identified based on the similarity of national ID codes and medical council codes. The number of active medical specialists was estimated by three-source capture-recapture method using Stata 12 software. RESULTS: A total of 33,416 specialists were identified from three sources. We estimated the number of specialists at 39127 (95% CI: 38823.6-39448.4) in 2015. Of these, 45.4% pertained to the province of Tehran while only less than 1.8% of specialists were in the provinces of Ilam (0.50%), South Khorasan (0.56%) and Kohgiloye and Boyerahmad (0.59%). The estimated ratio for specialists was 4.9 per 10000 population and ranged from 9.2 per 10000 in Tehran to 1.5 per 10000 population in Sistan and Balochestan. The overall completeness of data registries by three sources was 85.4%. CONCLUSION: The current distribution of specialists appears to be imbalanced. It is suggested to adopt appropriate policies to improve the distribution and maintenance of medical specialists in different parts of 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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".