A Prospective Study of Seasonal Variation in Shift‐Work Tolerance
Bibliographic record
Abstract
Seasonal effects on shift-work tolerance were assessed using the Standardized Shiftwork Index and the 21-item Hamilton Depression Scale. Participants (N=88) mainly worked a two-day, two-night, four-off rotation with 12 h shifts changing at 06:00 and 18:00 h in Vancouver, Canada. At this latitude (approximately 49 degrees N), daylength varies seasonally from approximately 16 to approximately 8 h, and both daily commutes occur in the dark in mid-winter and in sunlight in mid-summer. Questionnaires were completed twice, near the summer and winter solstices (order counterbalanced). Outcome variables were mood, general psychological health, sleep quality, chronic fatigue, physical health, job satisfaction, and social and domestic disruption. Of these, general psychological health and mood were significantly worse in winter, while sleep was more disturbed in summer. In winter, 31% exceeded the cutoff for psychological distress, and >70% scored in the higher than normal range for depressive symptoms. In summer, the proportions dropped to 19% and 53%, respectively. Measures of physical health and psychosocial well-being showed no seasonal effects. Relationships among explanatory and outcome variables, assessed by linear regression and canonical correlations, were also stable across season. Neuroticism was the strongest predictor of tolerance to shift work. Age was predictive only of sleep disturbance in both summer and winter. These results indicate that time of year can affect important outcome measures in shift-work assessment and intervention studies. The high average scores on measures of psychological distress and depression in winter suggest that at northern latitudes, some shift schedules may increase the risk of seasonal-type depression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".