Extent and nature of dual practice engagement among Iran medical specialists
Bibliographic record
Abstract
BACKGROUND: Dual practice (DP) by medical specialists is a widespread issue across health systems. This study aims to determine the level of DP engagement among Iran's specialists. METHODS: A pre-structured form was developed to collect the data about medical specialists worked in all 925 Iran hospitals in 2016. The forms were sent to the hospitals via medical universities in each province. The data were merged at the national level and matched using medical council ID codes, national ID codes, and eventually a combination of the first name, surname, and father's name. RESULTS: A total of 48 345 records were collected for 30 273 specialists from 858 (93%) hospitals out of total 925 hospitals. Sixteen thousand eight hundred forty-nine (69% of) specialists were non-faculty members and 6317 (26% of) specialists were employed on a contract basis. Eleven thousand six hundred and thirty-eight (47.7% of) specialists were engaged in DP on total. Female specialists had 0.78 times less DP chance; faculties compared to non-faculties had 0.65 times more DP chance and full-time geographic specialists compared to non-full-time specialists had 0.15 times more DP chance. DP was more frequent in specialists with higher age and more job experience and in provinces with more population, deprivation, and higher number of specialists per facility (P < 0.05). CONCLUSIONS: The level of DP is relatively high among Iran medical specialists, especially in geographic full-time specialists. However, they are totally banned and they receive extra payment for being full-time; restrictive regulations and financial incentives without considering other factors might not eliminate DP in specialists and it should be addressed based on conditions of each country and regions inside the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".