Quantifying cumulative circadian disruption from shift work and associations with health outcomes in a large cohort of nurses
Bibliographic record
Abstract
STUDY OBJECTIVES: Night shifts are commonly used as proxy for circadian disruption (CD) in epidemiological studies. However, other shift types can also cause CD if they interfere with a worker's biological night. We quantified and compared cumulative CD to night shift exposure and assessed their associations with health-related outcomes. METHODS: Shift work exposure was derived from questionnaire data for 42 119 nurses for the period 2012-2017. Cumulative CD was estimated as the total overlap (h) between shift work and preferred sleep-wake times. Pearson's correlation (r) assessed relationships between cumulative CD and night shift exposure. Associations with sleep disturbances, medication use, and overweight were analyzed using Poisson regression. RESULTS: The median cumulative CD among shift workers was 1674 h over 6 years (interquartile range = 432-3153 h). High CD (≥2809 h) was associated with increased prevalence of sleep problems (incidence rate ratio [IRR] = 1.10, 95% confidence interval [CI] 1.07-1.13), melatonin use (IRR = 1.86; 95% CI 1.70-2.04), sleep medication use (IRR = 1.15; 95% CI 1.01-1.32), and overweight (IRR = 1.04; 95% CI 1.02-1.07). The number of performed night shifts strongly correlated with cumulative CD (r = 0.93), and using night shifts as proxy for CD gave similar results. However, among shift workers who did not perform night shifts, high CD was still associated with increased sleep problems and melatonin use. CONCLUSION: Cumulative CD is associated with sleep and health disturbances, even among shift workers who do not perform night shifts, underlining its potential role in disease development. While night shifts remain a practical proxy in large-scale studies, our study highlights the importance of using more nuanced, individualized measures of CD.
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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) | 0.000 | 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".