The Impact of Duty Hour Limits on Sleep Quality of Resident: A Cross-sectional Study
Bibliographic record
Abstract
Objectives: Resident physicians are particularly prone to sleep disturbance due to long shift hours and excessive workload. Despite the numerous measures undertaken to improve their wellbeing, it is still unknown if limiting the work shift duration would improve sleep quality. We sought to compare sleep quality, anxiety, and depression before and after implementing duty hour limits. We also aim to evaluate how satisfaction with life is related to sleep quality, anxiety, and depression. Methods: We used a self-reported questionnaire to obtain data about sleep quality, anxiety, and depression using the Pittsburgh Sleep Quality Index and Patient Health Questionnaire-4 (PHQ-4) scales, respectively. Using data from a previous study, we compared those parameters before and after implementing duty hour reduction across several specialties at King Abdullah University Hospital, Jordan. Furthermore, we investigated residents’ life satisfaction using the Satisfaction with Life Scale. Results: One hundred and eighty residents filled the questionnaire (median age = 26.5 years). Males reported higher rates of poor sleep quality while females had higher rates of anxiety and depression. Decreasing the duration of on-call shifts from 32 to 24 hours decreased the prevalence of poor sleep quality from 91.5% to 83.2% (p =0.038), and smoking rates decreased from 30.4% to 12.5% (p < 0.001). More than six on-calls per month were associated with poorer quality of sleep. Night float shifts significantly decreased rates of moderate and severe PHQ-4 scores (p < 0.001). In addition, 63.3% of residents were satisfied with life. Life satisfaction was associated with enhanced sleep quality and lower PHQ-4 scores (p =0.007 and p < 0.001, respectively). Conclusions: Optimizing shift scheduling and duration can positively influence rates of sleep quality, anxiety, depression, and smoking. More interventions should be tackled along with duty hour limits to optimize residents’ life satisfaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".