Associations of Excessive Sleepiness on Duty with Sleeping Hours and Number of Days of Overnight Work among Medical Residents in Japan
Bibliographic record
Abstract
Despite long-standing concerns regarding the effects of working hours on the performance and health of medical residents, and the patients' safety, prior studies have not shown an association of excessive sleepiness with the number of sleeping hours and days of overnight work among medical residents. In August 2005, a questionnaire was mailed to 227 eligible participants at 16 teaching hospitals. The total number of sleeping hours in the last 30 d was estimated from the average number of sleeping hours during regular days and during days with overnight work, and the number of days of overnight work. Multiple logistic regression analysis was used to adjust for potentially associated variables. A total of 149 men and 47 women participated in this study. The participation rate was 86.3%. Among the participants, 55 (28.1%) suffered from excessive sleepiness. Excessive sleepiness was associated with sleeping for less than 150 h in the last 30 d (corrected odds ratio [cOR]=1.57; 95% confidence interval [CI], 1.02-2.16). The number of days of overnight work in the last 30 d showed no association with excessive sleepiness. Excessive sleepiness was also associated with smoking (cOR, 1.65; 95%CI, 1.01-2.32). Medical residents who slept for less than 150 h in the last 30 d and smoked had a significantly higher risk of excessive sleepiness on duty.
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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 | 0.001 | 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".