Bibliographic record
Abstract
BACKGROUND: Shift workers are at greater risk than day workers with respect to psychological and physical health, yet little research has linked shift work to increased sickness absence. AIMS: To investigate the relationship between shift work and sickness absence while controlling for organizational and individual characteristics and shift work attributes that have confounded previous research. METHODS: The study used archive data collected from three national surveys in Canada, each involving over 20000 employees and 6000 private-sector firms in 14 different occupational groups. The employees reported the number of paid sickness absence days in the past 12 months. Data were analysed using both chi-squared statistics and hierarchical regressions. RESULTS: Contrary to previous research, shift workers took less paid sickness absence than day workers. There were no differences in the length of the sickness absence between both groups or in sickness absence taken by female and male workers whether working days or shifts. Only job tenure, the presence of a union in the workplace and working rotating shifts predicted sickness absence in shift workers. The results were consistent across all three samples. CONCLUSIONS: In general, shift work does not seem to be linked to increased sickness absence. However, such associations may be true for specific industries. Male and female workers did not differ in the amount of sickness absence taken. Rotating shifts, regardless of industry, predicted sickness absence among shift workers. Consideration should be given to implementing scheduled time off between shift changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".