Night shift work exposure profile and obesity: Baseline results from a Chinese night shift worker cohort
Bibliographic record
Abstract
AIMS: This study aimed to evaluate the associations between types of night shift work and different indices of obesity using the baseline information from a prospective cohort study of night shift workers in China. METHODS: A total of 3,871 workers from five companies were recruited from the baseline survey. A structured self-administered questionnaire was employed to collect the participants' demographic information, lifetime working history, and lifestyle habits. Participants were grouped into rotating, permanent and irregular night shift work groups. Anthropometric parameters were assessed by healthcare professionals. Multiple logistic regression models were used to evaluate the associations between night shift work and different indices of obesity. RESULTS: Night shift workers had increased risk of overweight and obesity, and odds ratios (ORs) were 1.17 (95% CI, 0.97-1.41) and 1.27 (95% CI, 0.74-2.18), respectively. Abdominal obesity had a significant but marginal association with night shift work (OR = 1.20, 95% CI, 1.01-1.43). A positive gradient between the number of years of night shift work and overweight or abdominal obesity was observed. Permanent night shift work showed the highest odds of being overweight (OR = 3.94, 95% CI, 1.40-11.03) and having increased abdominal obesity (OR = 3.34, 95% CI, 1.19-9.37). Irregular night shift work was also significantly associated with overweight (OR = 1.56, 95% CI, 1.13-2.14), but its association with abdominal obesity was borderline (OR = 1.26, 95% CI, 0.94-1.69). By contrast, the association between rotating night shift work and these parameters was not significant. CONCLUSION: Permanent and irregular night shift work were more likely to be associated with overweight or abdominal obesity than rotating night shift work. These associations need to be verified in prospective cohort studies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".