PHYSICIAN HEALTH AND WELLNESS- ASSESSMENT OF A PEER SUPPORT PROGRAM IN A WESTERN CANADIAN TERTIARY HOSPITAL PEDIATRIC DEPARTMENT
Bibliographic record
Abstract
Physicians experience high rates of burnout due to the job related demands and emotional stressors . As a result, physician health and wellness initiatives have been sought out to mitigate burn out, and work towards advocating for physician well-being. Peer support programs have been used, and found to be effective, in mitigating burn out by utilizing the innate tendency to respond and empathized with shared difficulty. Our study evaluated the effectiveness of peer support and the recommendations needed to implement such a program on a larger scale. 14 physicians from the Regina General Hospital Pediatric department were paired to have informal virtual meetings (during COVID-19) every two weeks for three months. Following the program, physicians were individually interviewed and participated in a short cross sectional survey to understand the experience and perception of the program. Results showed a perceived benefit and value towards the program with an interest in continuing in a more formal fashion. The small department and the inability to meet naturally in person (due to COVID-19) provided limitations. Future indications of the program include continuation, expansion, and advocacy for the program. While providing a more formal structure with administrative support for schedule integration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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".