Precarious employment, strenuous working conditions and the long-term risk of diagnosed chronic musculoskeletal disorders
Bibliographic record
Abstract
OBJECTIVES: To investigate the effect of precarious employment (PE) on the risk of diagnosed chronic musculoskeletal disorders (MSDs) among Swedish workers in occupations with strenuous working conditions. METHODS: This nationwide register-based cohort study included workers registered as living in Sweden in 2005, aged 21-60 at the 2010 baseline. Three samples were included: workers with high biomechanical workload (n=680 841), repetitive work (n=659 422) or low job control (n=703 645). PE was evaluated using the SWE-ROPE (2.0) construct, which includes: contractual insecurity, temporariness, multiple jobs, income and collective bargaining agreement from 2010. Three exposure groups were created: PE, substandard and standard employment (SE). MSD data were obtained from outpatient registers (2011-2020). Cox proportional-hazards models estimated crude and adjusted sex-specific HRs with 95% CIs. Various outcomes were investigated for the different samples. RESULTS: Among workers with heavy biomechanical workload, results suggest increased risks of back MSDs in PE compared with those in SE. No association was found between PE and tendonitis in repetitive work, but PE was associated with an increased Carpal Tunnel Syndrome risk among men. Among workers with low job control, PE was associated with increased risks of soft tissue disorders among men and fibromyalgia among women. CONCLUSIONS: PE was associated with an increased risk of MSDs among workers with strenuous working conditions, with variations depending on disorder and sex. The findings suggest a differential exposure to biomechanical workload within occupations. Targeted interventions and strengthened workplace safety regulations are needed to protect the musculoskeletal health of workers in PE.
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 | 0.003 |
| 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 | 0.000 |
| 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".