Impact of work hours and sleep on well‐being and burnout for physicians‐in‐training: the Resident Activity Tracker Evaluation Study
Bibliographic record
Abstract
OBJECTIVE: The Resident Activity Tracker Evaluation (RATE) is a prospective observational study evaluating the impact of work hours, sleep and physical activity on resident well-being, burnout and job satisfaction. BACKGROUND: Physician burnout is common and its incidence is increasing. The impact of work hours and sleep on resident well-being and burnout remains elusive. Activity trackers are an innovative tool for measuring sleep and physical activity. METHODS: Residents were recruited from (i) general surgery and orthopaedics (SURG), (ii) internal medicine and neurology (MED) and (iii) anaesthesia and radiology (RCD). Groups 1 and 2 do not enforce restrictions on the duration of being on-call, and group 3 had restricted the duration of being on-call to 12 hours. Participants wore FitBit trackers for 14 days. Total hours worked, daily sleep, sleep on-call and daily steps were recorded. Participants completed validated surveys assessing self-reported well-being (Short-Form Health Survey), burnout (Maslach Burnout Inventory), and satisfaction with medicine. RESULTS: Surgical residents worked the most hours per week, followed by medical and RCD residents (SURG, 84.3 hours, 95% CI, 80.2-88.5; MED, 69.2 hours, 95% CI, 65.3-73.2; RCD, 52.2 hours, 95% CI, 48.2-56.1; p < 0.001). Surgical residents obtained fewer hours of sleep per day (SURG, 5.9 hours, 95% CI, 5.5-6.3; MED, 6.9 hours, 95% CI, 6.5-7.3; RCD, 6.8 hours, 95% CI, 5.6-7.2; p < 0.001). Nearly two-thirds of participants (61%) scored high burnout on the Maslach depersonalisation subscore. Total steps per day and well-being, burnout and job satisfaction were comparable between groups. Total hours worked, daily sleep and steps per day did not predict burnout or well-being. CONCLUSIONS: Work hours and average daily sleep did not affect burnout. Physical activity did not prevent burnout. Work hour restrictions may lead to increased sleep but may not affect resident burnout or 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".