Allowing failure so trainees can thrive: the importance of guided autonomy in medical education
Bibliographic record
Abstract
The delicate balance between resident autonomy and patient safety is an essential topic in medical education. Without a doubt, it is imperative to preserve the quality and safety of care for patients. As a result, clinician educators are constantly challenged by their obligation to provide the best possible patient care while educating the next generation of trainees. How are educators who are committed to patient safety but also want to prepare trainees for independent practice expected to educate within these constraints? Said differently, is it acceptable for an educator to allow a trainee to make a mistake under supervision to teach them to avoid mistakes in the future? In this issue of BMJ Quality and Safety , Klasen et al 1 summarise their findings from 19 semistructured interviews with clinical supervisors who allow their trainees to commit errors for educational purposes. The supervisors, who practice in a variety of procedural and non-procedural specialities in Switzerland and Canada, described a total of 79 examples of permitting clinical failure. The nature of these failures ranged widely from technical to communication to diagnostic errors and allowed for impactful, specific feedback. Specific themes of this feedback included sensory feedback to improve technical skills and emotional feedback to develop resilience. Supervisors were careful, …
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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 | 0.003 | 0.123 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".