Improving anesthesia resident wellness: a facilitated peer discussion group evaluated with a pre-/post-intervention survey
Bibliographic record
Abstract
Background: Many residents report a second victim response following near-miss events during their pediatric anesthesia rotation with consequences for their mental and physical wellbeing. This study investigated the impact of a Better Resident Wellness (BREW) initiative at our tertiary pediatric centre. Methods: We invited anesthesia residents to complete a survey at the start of their pediatric rotation. Questions included the Second Victim Experience and Support Tool (SVEST). During their rotation, residents attended weekly BREW rounds, a one-hour peer discussion facilitated by a psychologist. They provided feedback in a follow-up survey, including repeat SVEST. Results: 33/48 (69%) invited residents completed pre- and post-surveys Oct/2021-Feb/2023: all had attended one or more BREW rounds; 32/33 (97%) considered BREW helpful, safe, and would recommend to future residents; perceived benefits included improved morale (30/33, 91%) and clinical care (23/33, 70%). SVEST indicated a second victim response for 17/32 (53%) at the start and 7/32 (22%) at the end of their rotation (odds ratio 0.25, 95% CI 0.07 to 0.82, p = 0.019), with reduced professional self-efficacy concerns (median difference -0.25, 95%CI -0.50 to 0, p = 0.029). Conclusion: BREW offers anesthesia residents a desirable and beneficial support resource. Other residency programs should consider integrating facilitated peer discussion into their curriculum.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".