Residents, Responsibility, and Error: How Residents Learn to Navigate the Intersection
Bibliographic record
Abstract
PURPOSE: As a competency of Canadian postgraduate education, residents are expected to be able to promptly disclose medical errors and assume responsibility for and take steps to remedy these errors. How residents, vulnerable through their inexperience and hierarchical team position, navigate the highly emotional event of medical error is underexplored. This study examined how residents experience medical error and learn to become responsible for patients who have faced a medical error. METHOD: Nineteen residents from a breadth of specialties and years of training at a large Canadian university residency program were recruited to participate in semistructured interviews between July 2021 and May 2022. The interviews probed their experience of caring for patients who had experienced a medical error. Data collection and analysis were conducted iteratively using a constructivist grounded theory method with themes identified through constant comparative analysis. RESULTS: Participants described their process of conceptualizing error that evolved throughout residency. Overall, the participants described a framework for how they experienced error and learned to care for both their patients and themselves following a medical error. They outlined their personal development of understanding error, how role modeling influenced their thinking about error, their recognition of the challenge of navigating a workplace environment full of opportunities for error, and how they sought emotional support in the aftermath. CONCLUSIONS: Teaching residents to avoid making errors is important, but it cannot replace the critical task of supporting them both clinically and emotionally when errors inevitably occur. A better understanding of how residents learn to manage and become responsible for medical error exposes the need for formal training as well as timely, explicit discussion and emotional support both during and after the event. As in clinical management, graded independence in error management is important and should not be avoided because of faculty discomfort.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".