Exploring second victim syndrome among surgeons at the University of Toronto and assessing the need for peer support
Bibliographic record
Abstract
BACKGROUND: Second victim syndrome (SVS) is a term encompassing the psychological changes experienced by health care providers after an adverse event, and it is believed that nearly all surgeons experience SVS. We sought to explore SVS experiences of surgeons at our institution and assess the need for a peer support program. METHODS: We distributed a web-based questionnaire to staff surgeons at the University of Toronto, which covered demographics, the validated Second Victim Experience and Support Tool (SVEST), and interest in a peer support program. We performed quantitative assessment to determine at-risk demographics for SVS. RESULTS: We surveyed 120 participants. Scores on the SVEST were highest in the psychological distress and institutional support domains. There was no difference in scores by gender. General surgeons had higher scores in the turnover intentions dimension. Mid-career surgeons had higher scores in several dimensions. More than half of participants were in favour of a peer support program. CONCLUSION: Our results confirm that surgeons at our institution experience SVS, mainly in the form of psychological distress, and that most surgeons are interested in a peer support program. Further research to understand at-risk demographics and characterize survey nonresponders is warranted.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".