Bridging the gap: Responding to resident burnout and restoring well-being
Bibliographic record
Abstract
There is an increasing awareness of high burnout found among physicians. Resident physicians particularly face heightened stress due to inherent pressures of training in addition to systemic challenges common to healthcare. It is crucial that medical training programs and organizations create a culture which promotes physician well-being. We conducted an evaluation of a quality assurance pilot program aimed at creating a safe space for increasing burnout awareness and well-being among resident physicians. The program was voluntary, offered to psychiatry residents enrolled at McMaster University, and comprised an online resilience curriculum, peer groups, and wellness newsletters. Data analysis took place between December 15, 2018 and July 15, 2019. The educational goals were evaluated by outcome measures obtained over time in aggregated response data through residents' anonymous survey feedback. All aspects of the triad received positive feedback, with peer groups being perceived as most helpful. Of all residents, 31% (n = 22) engaged in all three aspects of the program; the majority were female (83%) and senior residents (63%). While 48% reported burnout upon enrollment, there was an average 50% stress reduction perceived post-attendance. This project has shown that peer groups can make a difference in the daily experience of psychiatry residents at our institution.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".