For better or worse? Changing shift schedules and the risk of work injury among men and women
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine the risk of work injury associated with changes in shift schedules and identify whether work injury risks differ between men and women. METHODS: Longitudinal panels from the Survey of Labor and Income Dynamics were used to describe work schedule patterns over a 6-year period among a representative sample of Canadian workers (N=19 131). Cox regression was used to estimate the risk of work injury among workers who (i) switched from regular day to nonstandard shifts, (ii) switched from nonstandard to day shifts and (iii) remained in nonstandard shifts, compared with (iv) those who worked regular day shifts only. Gender differences were examined in separate stratified analyses. Adjustments were made for potential respondent and occupational confounders. RESULTS: Increased injury risk was observed among those who: switched from day to nonstandard shifts [hazard ratio (HR) 2.60, 95% confidence interval (95% CI) 1.79-3.77], switched from nonstandard to days (HR 2.36, 95% CI 1.62-3.49), and worked nonstandard shifts only (HR 1.44, 95% CI 1.23-1.70). For women, work injury risk was higher among those who switched shifts (days to nonstandard HR 3.10, 95% CI 1.76-5.46; nonstandard to days HR 2.31, 95% CI 1.36-3.91), or worked nonstandard shifts only (HR 1.85, 95% CI 1.44-2.37) compared to day schedules. However, for men the risk of injury was elevated only among those who switched shifts (days to nonstandard HR 2.18, 95% CI 1.35-3.51; nonstandard to days HR 2.38, 95% CI 1.41-3.95). The only significant difference between men and women were among nonstandard shift workers. CONCLUSIONS: Our results suggest that changing shift types may increase work injury risk among men and women, and that the risk remains increased among women who work nonstandard shifts for a prolonged period of time. This highlights the need for awareness and implementation of health and safety programs when workers initially change shift schedules and on a regular basis to maintain worker health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".