0136 The Effect of Shift Type on Sleep before, during, and after Work in Rotating Shift Workers
Bibliographic record
Abstract
Atypical work schedules are associated with reduced sleep quantity and quality. Here, the objective was to determine the effect of shift type on total sleep time (TST) before, during, and after a series of consecutive shifts in rotating shift workers. A total of 2,589 days (range: 21-36 days/participant) of actigraphy data was available from 76 police officers involved in rotating shift work (M: 56, F: 20; age: 32 ± 5.4 y). The effect of shift type (morning, evening, night) on TST in the 24 h before, during, and after a series of consecutive shifts was determined using linear mixed-effect modelling. Participant was used as random effect and shift type, sleep timing (categorized as before, during, or after a series of consecutive shifts), and their interaction as fixed effects. Post-hoc pairwise comparisons were performed using Tukey’s test. Shift type and sleep timing as well as their interaction significantly affected 24-h TST (all p < 0.0001). Post-hoc analysis revealed a significant effect of shift type on TST in the 24 h prior to a first shift in a series (morning: 7.3 ± 0.15 h, evening: 7.8 ± 0.16 h, night: 8.8 ± 0.16 h [lsmeans ± SEM]; all p < 0.05), while no effect was found during a series of shifts (morning: 6.6 ± 0.15 h, evening: 6.9 ± 0.16 h, night: 6.5 ± 0.15 h; all p > 0.05). In the 24 h after the last shift in a series, TST was significantly higher for night shifts (10.3 ± 0.16 h) than for evening (7.5 h ± 0.16 h) and morning shifts (7.9 ± 0.15 h) (both p < 0.0001). A significant effect of shift type was found on 24-h TST in before and after, but not during, a series of consecutive work periods. Notably, we observed a significant sleep rebound after a series of night shifts in rotating shift workers. Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail and Fonds de Recherche du Québec-Santé.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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".