“I made a mistake!”: a narrative analysis of experienced physicians’ stories of preventable error
Bibliographic record
Abstract
Rationale, aims, and objectives: The complexity of healthcare systems makes errors unavoidable. To strengthen the dialogue around how physicians experience and share medical errors, the objective of this study was to understand how experienced generalist physicians make meaning of and grow from their medical errors. Methods: This study used a narrative inquiry approach to conduct and analyze in-depth interviews from 26 physicians from the generalist specialties of emergency, internal, and family medicine. We gathered stories via individual interview, analyzed them for key components, and rewrote a ‘meta-story’ in a chronological sequence. We conceptualized the findings into a metaphor to draw similarities, learn from, and apply new principles from other fields of practice. Results: Through analysis we interpreted the story of an elite athlete (physician) who is required to make numerous decisions in a short period of time within the construct of a chaotic sports field (clinical environment) among spectators (the patient’s family) whilst abiding by existing rules and regulations. Through sharing stories of success and failure, the team coach (clinical mentor) helps optimize the players’ professional and psychological development. Similarly, through sharing and learning from stories, team members (colleagues) and junior team members (trainees) also contribute to the growth of the protagonist’s character and the development of the overall team (clinic/hospital) and sport (healthcare system). Conclusion: We draw parallels between the clinical setting and a generalist physician’s experiences of a medical error with the environment and practices within professional sports. Using this comparison, we discuss the potential for meaningful coaching in medical education.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".