Evaluation of a participatory ergonomic intervention aimed at improving musculoskeletal health
Bibliographic record
Abstract
BACKGROUND: Participatory ergonomic (PE) interventions have been increasingly utilized to deal with work-related musculoskeletal disorders (WMSD). METHODS: Using a longitudinal quasi-experimental design, a PE process was launched at one depot of a large courier company, with a nearby depot serving as a control. Evaluations focused on 122 employees across the two depots who participated in both pre- and post-questionnaires. An evaluation framework assessed the process of implementation, changes in risk factors, and changes in musculoskeletal health outcomes. Partial and multiple regressions explored the relationships in the evaluation framework. RESULTS: Changes in work organizational factors had a consistent impact upon changes in health outcomes. Greater participation in the process was associated with increased levels of job influence and communication (P = 0.0059 and P = 0.0940 respectively). Improvements in communication levels were associated with reduced pain intensity and improved work role function (WRF) (P = 0.0077 and P = 0.0248 respectively). Lower levels of pain post-intervention were related to greater WRF (P = 0.0493). CONCLUSIONS: A PE approach can improve risk factors related to WMSD, and meaningful worker participation in the process is an important aspect for the success of such interventions.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".