Interaction between postural risk factors and job strain on self-reported musculoskeletal symptoms among users of video display units: a three-year prospective study
Bibliographic record
Abstract
OBJECTIVE: This study investigated a possible interaction between postural risk factors and job strain on the incidence proportion of self-reported musculoskeletal symptoms in the shoulder-neck, lower back and upper limbs regions. METHODS: A cohort of white-collar workers (N=2431) was assessed with a self-administered questionnaire regarding postural risk factors and job strain at work. After a three-year follow-up, the six-month incidence proportion of musculoskeletal symptoms in the three body regions was measured with a modified version of the Nordic questionnaire. The analyses were stratified for gender. Interaction was defined as a departure from the addition of effects of individual risk factors, and its importance was estimated from the attributable proportion due to interaction and its 95% confidence interval (95% CI). RESULTS: A significant attributable proportion of 0.80 (95% CI 0.23-1.37) due to interaction between postural risk factors and job strain was observed for men in the lower back region. An indication of interaction was found for women with attributable proportions due to interaction of 0.44 (95% CI -0.06-0.94), 0.27 (95% CI -0.34-0.88) and 0.36 (95% CI -0.33-1.05) for the shoulder-neck, lower back, and upper limbs regions, respectively. CONCLUSIONS: The simultaneous presence of postural risk factors and job strain seems to increase the pathogenic effect of each exposure on the incidence proportion of musculoskeletal symptoms. This interaction effect is important for work intervention practices as success in decreasing any of these two risk factors could have the additional benefit of reducing up to 80% of new cases of musculoskeletal symptoms among participants exposed to both risk factors.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".