Seeing but not believing: Insights into the intractability of failure to fail
Bibliographic record
Abstract
CONTEXT: Inadequate documentation of observed trainee incompetence persists despite research-informed solutions targeting this failure to fail phenomenon. Documentation could be impeded if assessment language is misaligned with how supervisors conceptualise incompetence. Because frameworks tend to itemise competence as well as being vague about incompetence, assessment design may be improved by better understanding and describing of how supervisors experience being confronted with a potentially incompetent trainee. METHODS: Following constructivist grounded theory methodology, analysis using a constant comparison approach was iterative and informed data collection. We interviewed 22 physicians about their experiences supervising trainees who demonstrate incompetence; we quickly found that they bristled at the term 'incompetence,' so we began to use 'underperformance' in its place. RESULTS: Physicians began with a belief and an expectation: all trainees should be capable of learning and progressing by applying what they learn to subsequent clinical experiences. Underperformance was therefore unexpected and evoked disbelief in supervisors, who sought alternate explanations for the surprising evidence. Supervisors conceptualised underperformance as: an inability to engage with learning due to illness, a life event or learning disorders, so that progression was stalled, or an unwillingness to engage with learning due to lack of interest, insight or humility. CONCLUSION: Physicians conceptualise underperformance as problematic progression due to insufficient engagement with learning that is unresponsive to intensified supervision. Although failure to fail tends to be framed as a reluctance to document underperformance, the prior phase of disbelief prevents confident documentation of performance and delays identification of underperformance. The findings offer further insight and possible new solutions to address under-documentation of underperformance.
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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 | 0.038 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".