Shift Work, Chronotype, and Melatonin Patterns among Female Hospital Employees on Day and Night Shifts
Bibliographic record
Abstract
BACKGROUND: Shift work-related carcinogenesis is hypothesized to be mediated by melatonin; however, few studies have considered the potential effect modification of this underlying pathway by chronotype or specific aspects of shift work such as the number of consecutive nights in a rotation. In this study, we examined melatonin patterns in relation to shift status, stratified by chronotype and number of consecutive night shifts, and cumulative lifetime exposure to shift work. METHODS: Melatonin patterns of 261 female personnel (147 fixed-day and 114 on rotations, including nights) at Kingston General Hospital were analyzed using cosinor analysis. Urine samples were collected from all voids over a 48-hour specimen collection period for measurement of 6-sulfatoxymelatonin concentrations using the Buhlmann ELISA Kit. Chronotypes were assessed using mid-sleep time (MSF) derived from the Munich Chronotype Questionnaire (MCTQ). Sociodemographic, health, and occupational information were collected by questionnaire. RESULTS: Rotational shift nurses working nights had a lower mesor and an earlier time of peak melatonin production compared to day-only workers. More pronounced differences in mesor and acrophase were seen among later chronotypes, and shift workers working ≥3 consecutive nights. Among nurses, cumulative shift work was associated with a reduction in mesor. CONCLUSION: These results suggest that evening-types and/or shift workers working ≥3 consecutive nights are more susceptible to adverse light-at-night effects, whereas long-term shift work may also chronically reduce melatonin levels. IMPACT: Cumulative and current exposure to shift work, including nights, affects level and timing of melatonin production, which may be related to carcinogenesis and cancer risk. Cancer Epidemiol Biomarkers Prev; 25(5); 830-8. ©2016 AACR.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".