‘Whatever you cut, I can fix it’: clinical supervisors’ interview accounts of allowing trainee failure while guarding patient safety
Bibliographic record
Abstract
BACKGROUND: Learning is in delicate balance with safety, as faculty supervisors try to foster trainee development while safeguarding patients. This balance is particularly challenging if trainees are allowed to experience the educational benefits of failure, acknowledged as a critical resource for developing competence and resilience. While other educational domains allow failure in service of learning, however, we do not know whether or not this strategy applies to clinical training. METHODS: We conducted individual interviews of clinical supervisors, asking them whether they allowed failure for educational purposes in clinical training and eliciting their experiences of this phenomenon. Participants' accounts were descriptively analysed for recurring themes. RESULTS: Twelve women and seven men reported 48 specific examples of allowing trainee failure based on their judgement that educational value outweighed patient risk. Various kinds of failures were allowed: both during operations and technical procedures, in medication dosing, communication events, diagnostic procedures and patient management. Most participants perceived minimal consequences for patients, and many described their rescue strategies to prevent an allowed failure. Allowing failure under supervision was perceived to be important for supporting trainee development. CONCLUSION: Clinical supervisors allow trainees to fail for educational benefit. In doing so, they attempt to balance patient safety and trainee learning. The educational strategy of allowing failure may appear alarming in the zero-error tolerant culture of healthcare with its commitment to patient safety. However, supervisors perceived this strategy to be invaluable. Viewing failure as inevitable, they wanted trainees to experience it in protected situations and to develop effective technical and emotional responses. More empirical research is required to excavate this tacit supervisory practice and support its appropriate use in workplace learning to ensure both learning and safety.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".