Reconceptualizing Ethics Through Morbidity and Mortality Rounds
Bibliographic record
Abstract
BACKGROUND: Surgeons face ethical tensions daily, yet ethics education continues to prove challenging. Two possible reasons for these challenges may be the different conceptions of knowledge between technical training vs those that underpin ethical practice, and the potential devaluing of ethics as a focus for education given false assumptions about its inherent nature. This study implemented and evaluated an innovation meant to prioritize and contextualize ethics in surgical learning and practice. STUDY DESIGN: After implementation of Ethics Morbidity and Mortality (M&M) rounds as an educational intervention, a qualitative evaluation consisted of interviews with 12 residents and 9 faculty. Analysis was informed by principles of constructivist grounded theory and the theoretical framework of Habermas' 3 types of knowledge: technical, practical, and emancipatory. For comparative purposes, analysis was conducted of how participants described ethics and ethics education and learning in relation to the traditional ethics teaching model vs the M&Ms. RESULTS: In the traditional model, ethics teaching was seen as disconnected from real life, and not valuable. Within M&Ms, ethics was viewed as integral to practice, engaging, valuable, and relevant. In the traditional model, ethics principles were seen as acquired through role modeling and as a fixed part of character. Within M&Ms, ethics principles were seen as learnable and transformable parts of identity. CONCLUSIONS: Traditional teaching of surgical ethics may result in physicians armed with knowledge, but unable to apply it. Our findings suggest that incorporating ethics into M&Ms allows not only learning the tools of ethics, but the knowledge that ethical principles were becoming integrated into professional identity.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".