‘It depends’: The complexity of allowing residents to fail from the perspective of clinical supervisors
Bibliographic record
Abstract
PURPOSE: Clinical supervisors acknowledge that they sometimes allow trainees to fail for educational purposes. What remains unknown is how supervisors decide whether to allow failure in a specific instance. Given the high stakes nature of these decisions, such knowledge is necessary to inform conversations about this educationally powerful and clinically delicate phenomenon. MATERIALS AND METHODS: 19 supervisors participated in semi-structured interviews to explore how they view their decision to allow failure in clinical training. Following constructivist grounded theory methodology, the iteratively collected data and analysis were informed by theoretical sampling. RESULTS: reflections, they could articulate four factors that they believed influenced these decisions: patient, supervisor, trainee, and environmental factors. While patient factors were reported as primary, the factors appear to interact in dynamic and nonlinear ways, such that supervisory decisions about allowing failure may not be predictable from one situation to the next. CONCLUSIONS: Clinical supervisors make many decisions in the moment, and allowing resident failure appears to be one of them. Upon reflection, supervisors understand their decisions to be shaped by recurring factors in the clinical training environment. The complex interplay among these factors renders predicting such decisions difficult, if not impossible. However, having a language for these dynamic factors can support clinical educators to have meaningful discussions about this high-stakes educational strategy.
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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".