Job strain and sickness absence among nurses in the province of Qu�bec
Bibliographic record
Abstract
BACKGROUND: Using Karasek's job strain model, the objective of the study was to determine whether nurses exposed to job strain had a higher incidence of sick leave than nurses not exposed. METHODS: The design was longitudinal. Data on sick leave were collected for 1,793 nurses for a 20-month period: short-term leaves and certified sick leaves. The Job Content Questionnaire was used to measure psychological demands, job decision latitude, and social support at work. RESULTS: Short-term sick leaves were associated with job strain (incidence density ratio (IDR) = 1.20) and with low social support at work (IDR = 1.26). Certified sick leaves were also significantly associated with low social support at work (IDR = 1.27 for all diagnoses and IDR = 1.78 for mental health diagnoses). CONCLUSIONS: Our results support the association between job strain and short-term sick leaves. The association with certified sick leaves is also significant for subgroups of nurses with specific job characteristics. Social support at work, although associated with all types of sick leaves measured, does not modify the association between job strain and absence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".