Initial self-reported data on sleep and burnout in pulmonary, critical care and sleep medicine: an initiative from the Assembly on Sleep and Respiratory Neurobiology of the American Thoracic Society
Bibliographic record
Abstract
RATIONALE: Health worker burnout has reached crisis proportions, threatening workforce sustainability. Limited data exist on the burden of burnout in pulmonary, critical care and sleep medicine (PCCSM), a high-demand and strained specialty. OBJECTIVE: At the Assembly of Sleep and Respiratory Neurobiology of the American Thoracic Society, we aimed to gather exploratory data on burnout in this group. METHODS: During a dedicated series of five virtual town halls (THs), we polled the audience regarding self-reported burnout. Topics included the scope of the problem, role of sleep, impact on clinical and academic operations, contributors in vulnerable groups, and mitigation strategies. RESULTS: A high proportion experienced burnout (45%) and 58% considered premature retirement. Insufficient sleep (53%) was common, most often due to excessive workload (57%) curtailing sleep through early morning meetings and electronic medical record (EMR) documentation. 36% also reported having a sleep disorder. Sleepiness (69%) and fatigue (58%) impaired work performance and patient care, and 54% reported a fatigue-related, personal-safety incident. Contributors to burnout in vulnerable communities included bias/discrimination (81%), harassment (44%) and assault (12%). Respondents predominantly endorsed organizational mitigating strategies: promoting a culture of "recovery time" (96%) and healthy sleep (86%), and periodic evaluation and accountability of leadership (86%). CONCLUSIONS: In this convenience sample of participants in a TH series regarding burnout in PCCSM, self-reported burnout was common. Sleep disturbance is a prevalent, under-recognized, but potentially modifiable contributor. The high reported rates of discrimination and harassment suggest that vulnerable groups may be at particular risk. To reduce burnout, system-level interventions aimed at transforming organizational culture and promoting leadership accountability were strongly endorsed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".