Leading teams while exhausted: Perspectives from healthcare epidemiology and beyond
Bibliographic record
Abstract
Mental fatigue and burnout are concerns for healthcare organizations, but their effects on leaders have not been thoroughly studied. Infectious diseases teams and leaders are at risk for mental fatigue and burnout due to the increased demands from the coronavirus disease 2019 (COVID-19) pandemic, additive effects of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) (omicron) and δ (delta) variant surges, and unique pre-existing pressures. No single intervention can reduce stress and burnout in healthcare workers. Work-hour limitations may have the biggest impact in physician burnout mitigation. Institutional and individual programs focused on mindfulness may improve well-being in the workplace. Leading during times of stress requires a multimodal approach and an understanding of goals and priorities. Greater awareness of burnout and fatigue across the healthcare spectrum and continued research are required to advance healthcare worker well-being.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".