Faculty-Wide Peer-Support Program During the COVID-19 Pandemic: Design and Preliminary Results
Bibliographic record
Abstract
BACKGROUND: Physicians experience higher rates of burnout relative to the general population. Concerns of confidentiality, stigma, and professional identities as health care providers act as barriers to seeking and receiving appropriate support. In the context of the COVID-19 pandemic, factors that contribute to burnout and barriers to seeking support have been amplified, elevating the overall risks of mental distress and burnout for physicians. OBJECTIVE: This paper aimed to describe the rapid development and implementation of a peer support program within a health care organization located in London, Ontario, Canada. METHODS: A peer support program leveraging existing infrastructures within the health care organization was developed and launched in April 2020. The "Peers for Peers" program drew from the work of Shapiro and Galowitz in identifying key components within hospital settings that contributed to burnout. The program design was derived from a combination of the peer support frameworks from the Airline Pilot Assistance Program and the Canadian Patient Safety Institute. RESULTS: Data gathered over 2 waves of peer leadership training and program evaluations highlighted a diversity of topics covered through the peer support program. Further, enrollment continued to increase in size and scope over the 2 waves of program deployments into 2023. CONCLUSIONS: Findings suggest that the peer support program is acceptable to physicians and can be easily and feasibly implemented within a health care organization. The structured program development and implementation can be adopted by other organizations in support of emerging needs and challenges.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".