Effectiveness of job rotation for preventing work-related musculoskeletal diseases: a cluster randomised controlled trial
Bibliographic record
Abstract
OBJECTIVE: Job rotation is an organisational strategy widely used on assembly lines in manufacturing industries to mitigate workers' exposure so as to prevent musculoskeletal disorders. This study aimed to evaluate the effectiveness of job rotation for reducing working hours lost due to sick leave resulting from musculoskeletal diseases. METHODS: The design consisted of a 1-year cluster randomised controlled trial with a blinded assessor. Production sectors of the textile industry were randomised to intervention and control groups. Both groups received ergonomic training. The intervention group performed a job rotation programme. The primary outcome measure was number of working hours lost due to sick leave as a result of musculoskeletal disease (ICD-10). The secondary outcome measures were musculoskeletal symptoms (Yes/No), risk factors for musculoskeletal diseases (0-10), psychosocial factors and fatigue (0-100), general health (0-100), and productivity (0-10). All secondary outcomes were measured at baseline and 12-month follow-up. RESULTS: At the 12-month follow-up, both groups showed an increase in the number of working hours lost due to sick leave for musculoskeletal disease. There was no significant difference between the job rotation intervention group (mean deviation -5.6 hours, 95% CI -25.0 to 13.8) at the 12-month follow-up and the control group. There were no significant differences between groups for the secondary outcomes (p>0.05). CONCLUSIONS: The job rotation programme was not effective in reducing the number of working hours lost due to sick leave, decreasing the prevalence of musculoskeletal symptoms, or improving perception of musculoskeletal pain and workplace risk factors, psychosocial risk factors and productivity. TRIAL REGISTRATION NUMBER: NCT01979731.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".