Job strain and neck–shoulder symptoms: a prevalence study of women and men white-collar workers
Bibliographic record
Abstract
BACKGROUND: Neck-shoulder symptoms are frequent among workers. Psychosocial factors at work have been associated with neck-shoulder symptoms, but few studies have examined job strain, the combined effect of high psychological demands (PD) and low decision latitude (DL). AIMS: To examine the association between psychosocial factors at work and the prevalence of self-reported neck-shoulder symptoms among white-collar workers. METHODS: In a cross-sectional study of 1543 white-collar workers, PD and DL at work were measured with Karasek's questionnaire. Prevalent cases were workers for whom neck-shoulder symptoms were present for >or=3 days during the previous 7 days and for whom pain intensity was greater than half the visual analogue scale. Gender and social support at work were evaluated as potential effect modifiers. RESULTS: Workers exposed to high job strain had a higher prevalence of neck-shoulder symptoms [adjusted prevalence ratio (PR): 1.54, 95% confidence interval (CI): 1.00-2.37]. No modifying effect of gender was observed in this association. The effect of job strain was stronger in workers with low social support (adjusted PR: 1.84, 95% CI: 0.92-3.68). These associations tended to be stronger and/or more precise when using alternative exposures and case definition. Namely, a stronger job strain effect was observed when a tertile cut-off was used to classify exposure (adjusted PR: 2.47, 95% CI: 1.15-5.32). CONCLUSION: These results suggest that primary prevention of neck-shoulder symptoms among white-collar workers should consider the exposure to job strain, especially when workers are exposed to low social support at work.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".