Preparing Medical Students and Physicians to Cope With Their Medical Errors: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Many clinicians will experience feeling responsible for inadvertently harming a patient through medical error. These situations can be distressing, difficult to navigate, and career altering. In addition to aftermath support, there is a recognized need for preemptive education to better prepare clinicians to cope after such events. Little published guidance exists about how best to do this. This scoping review explores the current knowledge about educational programs and strategies to prepare medical students and physicians to cope effectively with their own medical errors. METHOD: Three online databases, MEDLINE, PsycINFO, and Scopus, were searched on January 3, 2025, for articles published from database inception to the search date that described programs designed to prepare medical students and/or physicians to cope better with personal involvement in patient harm from medical error. Eligibility screening and data recording were independently performed by 2 reviewers. Simple data were summarized, and common pedagogical strategies were identified by inductive thematic analysis. RESULTS: The search yielded 5,359 unique articles for screening, of which 97 full-text articles were retrieved. Twelve articles met the eligibility criteria. The study interventions varied in structure and delivery methods but shared similar rationale, key messages, and pedagogical strategies. Dominant pedagogical strategies were divided into 2 groups: (1) what students need to learn about coping after medical error (attend to emotions, universality of fallibility, helpful and unhelpful paths for coping, workplace culture, care for colleagues, transfer of learning to the workplace) and (2) ways to support students in this learning (actual cases, psychological safety and honest conversation, role-modeling, reflection and learning from others' experience). CONCLUSIONS: A constellation of pedagogical strategies synthesized from the literature provides program design ideas for education that targets the development of learners' capacity to cope with involvement in patient harm from medical error.
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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 | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| 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".