How compatible are participatory ergonomics programs with occupational health and safety management systems?
Bibliographic record
Abstract
OBJECTIVES: Musculoskeletal disorders (MSD) are a major cause of pain, disability, and costs. Prevention of MSD at work is frequently described in terms of implementing an ergonomics program, often a participatory ergonomics (PE) program. Most other workplace injury prevention activities take place under the umbrella of a formal or informal occupational health and safety management system (OHSMS). This study assesses the similarities and differences between OHSMS and PE as such knowledge could help improve MSD prevention activities. Methods Using the internationally recognized Occupational Health and Safety Assessment Series (OHSAS 18001), 21 OHSMS elements were extracted. In order to define PE operationally, we identified the 20 most frequently cited papers on PE and extracted content relevant to each of the OHSAS 18001 elements. RESULTS: The PE literature provided a substantial amount of detail on five elements: (i) hazard identification, risk assessment and determining controls; (ii) resources, roles, responsibility, accountability, and authority; (iii) competence, training and awareness; (iv) participation and consultation; and (v) performance measurement and monitoring. However, of the 21 OHSAS elements, the PE literature was silent on 8 and provided few details on 8 others. CONCLUSIONS: The PE literature did not speak to many elements described in OHSMS and even when it did, the language used was often different. This may negatively affect the effectiveness and sustainability of PE initiatives within organizations. It is expected that paying attention to the approaches and language used in management system frameworks could make prevention of MSD activities more effective and sustainable.
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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 | 0.071 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".