Factors Associated with Dual Practice in Surgery Specialists: Application of Multi-Level Analysis on National Registry Data
Bibliographic record
Abstract
BACKGROUND: Dual practice by surgery specialists is a widespread issue across health systems. This study aimed to determine the level of dual practice engagement and its related factors among Iran's surgery specialists. METHODS: A pre-structured form was developed to collect the data about surgery 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. Multilevel logistic regression was used to assessing the association between dual practice with study variables. RESULTS: 93% response rate) and 6405 (57% of) engaged in DP on total. Urinary tract & genital and neurosurgery specialties had the highest rank with 69%. DP was more frequent in specialists with higher age and experience, populated provinces, higher deprivation, and share of private hospitals. Faculty physicians (OR=0.69), full-time geographic physicians (OR=0.17), specialists with more than 25 years' experience (OR=2.59) and age more than 40 yr (OR=1.3) had significant association with dual practice. CONCLUSION: Multi-approach strategy is needed to control dual practice through tax regulations, income cap, and limitations in work hours and number of visits in private sector.
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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 | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".