Stress and burnout in anesthesia residency: a case study of peer support groups
Bibliographic record
Abstract
Stress and burnout are alarmingly prevalent in anesthesiologists, with the highest risk occurring during anesthesia residency training. To better understand this phenomenon, we conducted a mixed methods case study of our anesthesia training program to explore the residents’ accounts of stress and burnout and the potential value of peer support groups. Eight out of thirty eight residents participated in nine monthly peer support group (PSG) meetings followed by a focus group interview about stress and burnout in training and the value of PSG. We compared the participants’ mean pre-and post-PSG Maslach Burnout Inventory® (MBI) and Perceived Stress Scale® (PSS) and analysed the focus group interview for recurring themes. We captured the perspectives of twenty seven out of thirty residents who did not participate in support groups (non-participants) through an online survey on stress and burnout. We found evidence of a high prevalence of stress and burnout from the MBI and PSS scores and survey responses. Analysis of the focus group interview showed that the specific stressors of anesthesia training included: an individually-based model of training that predisposes to isolation from peers, an over-reliance on the quality of the faculty-resident relationship and the critical, high stakes nature of the profession. Residents strongly endorsed the value of PSG in decreasing isolation, enhancing validation, and support through the sharing of experiences. Lack of dedicated time and integration into the training program were major barriers to PSG participation. These barriers need to be overcome in order to fully realize its role in mitigating stress and burnout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".