Assessment of surgery residents' knowledge of medical ethics and law. Implications for training and education
Bibliographic record
Abstract
Medical ethics and law are essential topics that should be included in medical residency programs. However, surgery training programs in Iran lack a specific course in medical ethics and law, which can lead to patient dissatisfaction with surgical outcomes. This study aimed to assess surgery residents' knowledge of medical ethics and law and suggest improvements for future residency programs. This descriptive cross-sectional study involved 112 surgery residents from six teaching hospitals. A valid and reliable questionnaire comprising 15 items on medical ethics and 12 items on medical law was used to assess participants' knowledge. Most participants were female (31-40 years old), and their mean knowledge score for medical ethics was 3.26±0.53 out of 5, with the lowest score in "futile treatment and DNR orders." The mean knowledge score for medical law was 3.69±0.69, with the lowest score in "surrogate decision-maker." Age did not affect residents' knowledge, but gender did, with female residents demonstrating significantly better knowledge of medical ethics (3.344/5 vs. 3.112/5) and law (3.789/5 vs. 3.519/5). Surgery residents had a relatively favorable knowledge of medical ethics and law, but they require further training in some areas to improve their knowledge. Training should include journal clubs, role-play programs, standardized patient programs, and debates to achieve better results, as purely didactic lectures appear inadequate.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".