The Shame–Blame Game: Is It Still Necessary? A National Survey of Shame-based Teaching Practice in Canadian Plastic Surgery Programs
Bibliographic record
Abstract
BACKGROUND: As understanding of poor physician mental health and burnout strengthens, it is becoming important to identify factors that detract from wellbeing. Shame-based learning (SBL) is detrimental to psychological health and can contribute to burnout, substance abuse and suicide. This study endeavoured to quantify the unknown prevalence and effects of SBL in Canadian plastic surgery programs. METHODS: An electronic survey was sent to all attending surgeons and trainee (residents and fellows) members of the Canadian Society of Plastic Surgeons. SBL was assessed using a validated questionnaire. Data was analyzed using descriptive statistics and thematic analysis. RESULTS: 98 responses (14.7%) comprising of 63 attending surgeons and 36 trainees were received. The majority of attending surgeons (78 percent) and trainees (67%) have been shamed. Fourteen percent of trainees and 9% of attending surgeons felt that SBL is necessary. The most common event provoking shaming for trainees was wrong answers (56%) and for attending surgeons was disagreement in clinical care (21%). For both groups, shamers were in positions of authority. The most common effect of SBL in trainees was a loss of self-confidence (53%), compared to no negative effect in attending surgeons (49 percent). Thirty-nine percent of trainees dealt with shaming events with support from fellow trainees (39 percent), while attending surgeons kept it to themselves (40 percent). CONCLUSION: SBL is present in Canadian plastic surgery residency programs and has numerous detrimental effects. To foster better mental health, residency programs should identify ongoing SBL and make efforts to transition to healthier feedback strategies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".