0229 Sleep Duration Affects Subjective Alertness and Sleepiness of Police Officers on Rotating Shifts
Bibliographic record
Abstract
Sleep deprivation has been associated with reduced alertness and increased sleepiness during different types of shift, affecting shift workers’ performance and well-being at work. The aim of the present study was to assess whether the amount of time slept prior to a shift affects workers’ subjective alertness and sleepiness levels at the shift start. A total of 25 municipal police officers from PQ, Canada, (17 men, 8 women) aged 31.3 ± 4.5 years (mean ± SD) working morning, evening, and night shifts of 9- to 12-h duration, were enrolled to a 35-day field study. Subjective alertness (VAS) and sleepiness (Karolinska Sleepiness Scale) levels were documented at the start of each shift. For each work shift, actigraphically-recorded total sleep time (TST) of the main sleep period prior to that shift was used in the analysis. The effect of TST, shift type, and their interaction were analyzed using linear mixed-effect models with TST and shift type as a factor. Alertness levels were lower (p<0.001) at the start of morning vs. evening shifts (p=0.01). Sleepiness levels were higher (p<0.001) at the start of morning vs. evening shifts (p<0.001), and of evening vs. night shifts (p<0.05). Participants’ TST (mean ± SD) was 5.99 ± 0.9, 6.9 ± 1.0, and 6.1 ± 0.9 hours, prior to morning, evening, and night shifts, respectively. A significant TST x shift interaction was observed on alertness (p<0.001) and sleepiness (p<0.001) levels. Specifically, alertness increased and sleepiness decreased as a function of TST prior to morning and evening shifts. Our results suggest that TST influences shift workers’ alertness and sleepiness levels at the start of a shift, and thus fitness for duty. No such relation was observed at the start of night shifts, possibly due to the circadian crest of vigilance occurring at that time. Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST)
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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 | 0.001 |
| 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".