Development of Professional Ethics Curriculum in the Operating Room for the Current Era of Surgery: A Mixed Method Study
Bibliographic record
Abstract
Introduction: The rapid advancement of intricate technologies and the emergence of novel surgical methodologies necessitate nuanced ethical decision-making under high-stress scenarios. Consequently, cultivating an understanding of professional ethics within the surgical environment is crucial for all practitioners involved in patient care. This study was initiated with the aim of designing a comprehensive curriculum for Iranian medical schools, focusing on professional ethics within the operating room. Methods: This mixed-method exploratory research was executed in distinct qualitative and quantitative phases. The first stage involved conducting 12 structured interviews with Iranian faculty members who were experts in education of professional ethics and operating room staffs for assessing the current needs and reviewing extant curricula. The subsequent quantitative phase entailed evaluating the elements of each curriculum axis via the Delphi method. Results: The qualitative phase led to the identification of 45 primary codes, 14 subcategories, and 5 primary categories. The quantitative phase confirmed 3 instructional goal domains, 12 instructional content areas, 8 teaching methodologies, and 10 evaluation methods through the Delphi process. These confirmed components were eventually incorporated into various theoretical and clinical courses as longitudinal integration themes. Conclusion: Based on our findings, we recommend the development of educational objectives targeting cognitive, affective, and psychomotor domains and the longitudinal integration of a professional ethics course.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".