The shift from disbelieving underperformance to recognising failure: A tipping point model
Bibliographic record
Abstract
CONTEXT: Coming face to face with a trainee who needs to be failed is a stern test for many supervisors. In response, supervisors have been encouraged to report evidence of failure through numerous assessment redesigns. And yet, there are lingering signs that some remain reluctant to engage in assessment processes that could alter a trainee's progression in the programme. Failure is highly consequential for all involved and, although rare, requires explicit study. Recent work identified a phase of disbelief that preceded identification of underperformance. What remains unknown is how supervisors come to recognise that a trainee needs to be failed. METHODS: Following constructivist grounded theory methodology, 42 physicians and surgeons in British Columbia, Canada shared their experiences supervising trainees who profoundly underperformed, required extensive remediation or were dismissed from the programme. We identified recurring themes using an iterative, constant comparative process. RESULTS: The shift from disbelieving underperformance to recognising failure involves three patterns: accumulation of significant incidents, discovery of an egregious error after negligible deficits or illumination of an overlooked deficit when pointed out by someone else. Recognising failure was accompanied by anger, certainty and a sense of duty to prevent harm. CONCLUSION: Coming to the point of recognising that a trainee needs to fail is akin to the psychological process of a tipping point where people first realise that noise is signal and cross a threshold where the pattern is no longer an anomaly. The co-occurrence of anger raises the possibility for emotions to be a driver of, and not only a barrier to, recognising failure. This warrants caution because tipping points, and anger, can impede detection of improvement. Our findings point towards possibilities for supporting earlier identification of underperformance and overcoming reluctance to report failure along with countermeasures to compensate for difficulties in detecting improvement once failure has been verified.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".