An Investigation of Ergonomics Analysis Tools Used in Industry in the Identification of Work-Related Musculoskeletal Disorders
Bibliographic record
Abstract
Web-based surveys were sent to Canadian certified ergonomists, Joint Health and Safety Committees (JHSCs) and health and safety certification trainers to understand better which ergonomics analysis tools were used in industry and help JHSCs obtain the necessary training required to reduce work-related musculoskeletal disorders (WMSDs). The results showed that most of the certified ergonomists used the Snook/Mital tables, the National Institute of Occupational Safety and Health (NIOSH) equation and rapid upper limb assessment (RULA) /rapid entire body assessment (REBA). The most frequently used methods by JHSCs to identify ergonomics risk were injury reports and worker complaints. The surveys for the health and safety certification trainers revealed that most curricula did not include ergonomics analysis tools. There appears to be a gap between what is recommended by certified ergonomists for JHSC, what is taught in training and what is used by JHSCs for ergonomics risk analysis. A better understanding, modifications in training curricula and education of JHSCs are needed to help reduce WMSDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".