Actigraph measures of sleep among female hospital employees working day or alternating day and night shifts
Bibliographic record
Abstract
Sleep disturbance is common among shift workers, and may be an important factor in the effect of shift work on chronic disease development. In this cross-sectional study, we described sleep patterns of 294 female hospital workers (142 alternating day-night shift workers, 152 day workers) and determined associations between shift work and sleep duration. Rest-activity cycles were recorded with the ActiGraph GT3X+ for 1 week. Analyses were stratified by chronotype of shift workers. Using all study days to calculate average sleep duration, shift workers slept approximately 13 min less than day workers during main sleep periods, while 24-h sleep duration did not differ between day workers and shift workers. Results from age-adjusted models demonstrated that all shift workers, regardless of chronotype, slept 20-30 min less than day workers on day shifts during main and total sleep. Early and intermediate chronotypes working night shifts slept between 114 and 125 min less than day workers, both with regard to the main sleep episode and 24-h sleep duration, while the difference was less pronounced among late chronotypes. When sleep duration on free days was compared between shift workers and day workers, only shift workers with late chronotypes slept less, by approximately 50 min, than day workers during main sleep. Results from this study demonstrate how an alternating day-night shift work schedule impacts sleep negatively among female hospital workers, and the importance of considering chronotype in sleep research among shift workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".