Health-related interventions among night shift workers: a critical review of the literature
Bibliographic record
Abstract
OBJECTIVES: Associations between shift work and chronic disease have been observed, but relatively little is known about how to mitigate these adverse health effects. This critical review aimed to (i) synthesize interventions that have been implemented among shift workers to reduce the chronic health effects of shift work and (ii) provide an overall evaluation of study quality. METHODS: MeSH terms and keywords were created and used to conduct a rigorous search of MEDLINE, CINAHL, and EMBASE for studies published on or before 13 August 2012. Study quality was assessed using a checklist adapted from Downs & Black. RESULTS: Of the 5053 articles retrieved, 44 met the inclusion and exclusion criteria. Over 2354 male and female rotating and permanent night shift workers were included, mostly from the manufacturing, healthcare, and public safety industries. Studies were grouped into four intervention types: (i) shift schedule; (ii) controlled light exposure; (iii) behavioral; and, (iv) pharmacological. Results generally support the benefits of fast-forward rotating shifts; simultaneous use of timed bright light and light-blocking glasses; and physical activity, healthy diet, and health promotion. Mixed results were observed for hypnotics. Study quality varied and numerous deficiencies were identified. CONCLUSIONS: Except for hypnotics, several types of interventions reviewed had positive overall effects on chronic disease outcomes. There was substantial heterogeneity among studies with respect to study sample, interventions, and outcomes. There is a need for further high-quality, workplace-based prevention research conducted among shift workers.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".