Circadian Adaptation of Melatonin and Cortisol in Police Officers Working Rotating Shifts
Bibliographic record
Abstract
Misalignment of behavior and circadian rhythms due to night work can impair sleep and waking function. While both simulated and field-based studies suggest that circadian adaptation to a nocturnal schedule is slow, the rates of adaptation in real-world shift-work conditions are still largely unknown. The aim of this study was to evaluate the extent of adaptation of 24-h rhythms with 6-sulfatoxymelatonin (aMT6s) and cortisol in police officers working rotating shifts, with a special attention to night shifts. A total of 76 police officers (20 women; aged 32 ± 5.4 years, mean ± SD) from the province of Quebec, Canada, participated in a field study during their 28- or 35-day work cycle. Urine samples were collected for ~32 h before a series of day, evening, and night shifts to assess circadian phase. Before day, evening, and night shifts, 60%-89% of officers were adapted to a day schedule based on aMT6 rhythms, and 71%-78% were adapted based on cortisol rhythms. To further quantify the rate of circadian adaptation to night shifts, initial and final phases were determined in a subset of 37 officers with suitable rhythms for both hormones before and after 3-8 consecutive shifts (median = 7). Data were analyzed with circular and linear mixed-effects models. After night shifts, 30% and 24% of officers were adapted to a night-oriented schedule for aMT6s and cortisol, respectively. Significantly larger phase-delay shifts (aMT6s: -7.3 ± 0.9 h; cortisol: -6.3 ± 0.8 h) were observed in police officers who adapted to night shifts than in non-adapted officers (aMT6s: 0.8 ± 0.9 h; cortisol: 0.2 ± 1.1 h). Consistent with prior research, our results from both urinary aMT6s and cortisol midpoints indicate that a large proportion of police officers remained in a state of circadian misalignment following a series of night shifts in dim-light working environments.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".