How Physicians Learn to Say "I'm Sorry": Power, Culture, and Apology in Medical Education
Bibliographic record
Abstract
Background: Apologizing after a medical error is a vital component of ethical, patient-centered care. Sincere apologies can restore trust, reduce distress, and support healing. Yet the ability to apologize is not instinctive, it is shaped by institutional culture, power dynamics, and educational exposure. Despite increasing emphasis on disclosure training, no prior synthesis has thoroughly examined how medical students are taught to apologize or how sociocultural factors influence this learning. This scoping review explores how medical students learn to apologize in clinical settings, focusing on formal curricula, faculty role modeling, institutional norms, and emotional skill development.Method: Using Arksey and O’Malley’s framework, refined by Levac et al., and reported per PRISMA-ScR guidelines, we searched PubMed, MEDLINE, Scopus, ERIC, and Google Scholar. Peer-reviewed articles published in English from 2000-2024 were included if they addressed apology or error disclosure in undergraduate medical education. Two reviewers conducted independent screening and data extraction. Studies were thematically analyzed across five domains: curriculum, faculty role modeling, institutional culture, emotional skills, and outcomes.Results: Seventeen studies met inclusion criteria. Interventions such as simulations, communication frameworks, and patient safety exercises improved students’ confidence in disclosure. Faculty role modeling had strong influence, though observed apologies were often inadequate. Hidden curricula and hierarchies hindered authentic communication. Empathy training facilitated sincere apologies, yet few programs assessed long-term behaviors or addressed structural barriers.Conclusion: Teaching apology in medicine requires more than communication skills, it demands longitudinal, systems-based efforts that foster humility, transparency, and institutional accountability.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".