How Medical Error Shapes Physicians’ Perceptions of Learning: An Exploratory Study
Bibliographic record
Abstract
PURPOSE: Error is inevitable in medicine, given its inherent uncertainty and complexity. Errors can teach powerful lessons; however, because of physicians' self-imposed silence and the intricacies of responsibility and blame, learning from medical error has been underexplored. The purpose of this study was to understand how physicians perceived learning from medical errors by exploring the tension between responsibility and blame and factors that affected physicians' learning. METHOD: Nineteen physicians participated in semistructured interviews, conducted in 2016-2017 at Western University in Canada, that probed their experiences in learning from medical errors. Data collection and analysis were conducted iteratively, with themes identified through constant comparative analysis. RESULTS: Participants felt personal responsibility and blame for their errors. Residency produced particularly salient memories of errors. Participants identified interconnecting cultural factors (normalizing error, peer support and mentorship, formal rounds) and individual factors (emotional response, confidence and experience), which either helped or hindered their perceived learning. CONCLUSIONS: Learning from medical error requires navigation through blame and responsibility. The keen responsibility felt by physicians must be acknowledged when enacting a system-based approach to medical error. Adopting a learning culture perspective suggests opportunities to enable and disable features of the learning environment to optimize learning from error as residents learn to become the most responsible physician for all outcomes. A better understanding of the factors that shape learning from error can help make the transition from error to learning more explicit, thereby increasing the opportunity to learn and teach from errors that permeate the practice of medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".