The Emotional Impact and Coping Mechanisms Following Adverse Patient Events Among Canadian Vascular Surgeons and Trainees
Bibliographic record
Abstract
BACKGROUND: This study's objective is to evaluate the emotional experiences, coping mechanisms, and support resources for Canadian vascular surgeons and trainees following an adverse patient event or near miss. METHODS: This is a cross-sectional survey study of all Canadian Society for Vascular Surgery (CSVS) members from October to November 2021. We collected data on participant experiences with adverse events, their emotional responses, the coping mechanisms used, and their perceptions on available support resources. RESULTS: The survey was sent to 233 CSVS members yielding 66 responses. The majority (77%) of respondents had experiences with adverse event causing serious patient harm. The most common negative experience following an adverse event included feelings of negativity towards oneself, general distress, and anxiety about potential for future errors. The most common coping mechanism was seeking advice from a mentor or close colleague. Peers (82%) and senior colleagues (59%) were the most preferred sources of support. Most of the respondents would reach out to a mentor if they had 1, but 30% reported no mentor or close colleague for support. CONCLUSION: Adverse patient events and near misses have serious negative impact on the lives of Canadian vascular surgeons and trainees. Peers and senior colleagues are the most desired source for support, but this is not universally available. Organized efforts are needed to bring awareness in our vascular surgery community on the ubiquitous nature and detrimental effects of adverse events.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".