Building community during the COVID-19 pandemic: a system level approach to physician well-being
Bibliographic record
Abstract
Before the COVID-19 pandemic, physician burnout was identified as reaching crisis proportions, and the pandemic is expected to worsen the already perilous state of physician wellness. It has affected physicians’ emotional health, not only by increasing workload demands, but also by eroding resilience under increasing pressures. The mental health consequences are expected to persist long after the pandemic subsides. With physician wellness increasingly recognized as a shared responsibility between individual physicians and the health care system, system-level approaches have been identified as important interventions for addressing physician well-being. In this article, we describe two evidence-guided initiatives implemented in our hospitalist network during the current pandemic: a trained peer-support team and facilitated physician online group discussions. These initiatives acknowledge the emotional strain of physicians’ work and challenge the “iron doc” culture of medicine. Our efforts build community and shift culture toward improved physician wellness. We suggest that the pandemic might be an opportunity for our profession to strengthen our support networks and for physician leaders to advance physician wellness in their work environments.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| 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".