Effect of dual practice on service delivery time by surgeons in the Islamic Republic of Iran: multi-level analysis of a national survey, 2016
Bibliographic record
Abstract
BACKGROUND: One of the work patterns which affects the supply of specialists is the phenomenon of dual practice (DP), i.e., working simultaneously in the public and private sectors. Uncontrolled DP in the surgery health workforce can have adverse effects on access to surgeons, efficiency, effectiveness and quality of surgery services. AIMS: The aim of this article is to examine the impact of DP on service delivery time by surgeons. METHODS: We used a prestructured form to collect data on surgery specialists in all 925 Iranian hospitals. National medical ID codes, council ID codes, first name, surname and father's name were used for data matching. Multilevel linear regression was used to assess the association between DP and study variables, which were recruitment type, faculty status, experience, sex and age. RESULTS: The 4642 surgery specialists in this study, representing 31.08% of the total number of surgeons identified, spent mean 1.09 (standard deviation 0.33) hours full-time equivalent (FTE) on health care service delivery. Specialists with DP had long service delivery time (β = 0.427). Female specialists (β = -0.049) and full-time specialists (β = -0.082) spent less time on health care service delivery. Permanent specialists had higher FTE (P < 0.001) and as the population increases, FTE increases (P < 0.05). CONCLUSIONS: Although DP had a direct impact on surgeons' working hours, it seems that a greater share of the difference in working time was used in the private sector services, leading to poor access to surgery services in the public sector. Therefore, it is necessary to develop a systems approach to regulate DP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".