0777 Variations in Vigilance and Sleep Among Underground Mine Workers During 14 Consecutive Night Shifts
Bibliographic record
Abstract
Abstract Introduction Adverse effects of night shift work are well known but there is scarce data on how vigilance and sleep vary across a large number of consecutive night shifts. Methods In summer, 38 underground miners (mean age (SD): 36.8 (13.9) years) wore an actigraph, filled out the Morningness-Eveningness questionnaire, and completed a Visual Analog Scale capturing subjective vigilance (very sleepy to very alert) 4 times per shift (19:00, 22:00, 02:00, and 05:30) for 14 consecutive night shifts. Mixed effects linear regression models were used to account for repeated measures. Results Mean vigilance level is lower at 22:00, 02:00 and 05:30 than at the beginning of the shift at 19:00 (p<0.001). Also, a more pronounced decrease in vigilance during the night was observed among older workers compared to younger workers (p<0.05). Moreover, workers with greater eveningness have higher vigilance at the beginning of the first night shift at 19:00 (p<0.001), but their decline in vigilance level during the night is faster than that observed in workers with greater morningness (p<0.01). Interestingly, the mean vigilance decline observed at 02:00 and 05:30 (compared to 19:00) is slowed down for each additional night shift (p<0.001). Furthermore, mean sleep efficiency is negatively associated with morningness and gradually decreases across consecutive night shifts (p<0.05). In addition, mean sleep duration is shorter in older workers and is positively associated with morningness (p<0.05). Conclusion Results show a progressive improvement in vigilance of mine workers assigned to a large number of consecutive 12-hour night shifts from 2 am onwards. This may probably be ascribed to an adjustment in homeostatic sleep propensity consecutive to the partial sleep deprivation associated with time spent traveling to the remote site before the first shift. However, circadian adjustment is unlikely considering the strong morning light exposure experienced daily after the night shift. Support College and Community Innovation Program of the Natural Sciences and Engineering Research Council of Canada (NSERC) (CUI2I 472201-14)
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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".