A comparison of RULA, REBA and Strain Index to four psychophysical scales in the assessment of non-fixed work
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to evaluate the efficacy of three ergonomic assessment tools (EATs) (RULA, REBA and Strain Index (SI)) in the assessment of non-fixed work through comparison to four occupationally relevant Borg 10 psychophysical scales: Lifting Effort, Grasping Effort, Wrist Discomfort, and Low Back Discomfort. PARTICIPANTS: Fourteen male rodworkers participated in this study. The participants had at least six months experience and had no musculoskeletal injuries in the six months preceding their participation. METHODS: Psychophysical scale and video data were collected while participants performed non-fixed work on construction sites. Psychophysical and EAT outcome measure scores were calculated for a shortlist of tasks. RESULTS: It was found that the perceived Grasping Effort and Wrist Discomfort scales differentiated between the WMSD risks associated with rodworking tasks and SI was found to be more effective than RULA and REBA in the assessment of non-fixed work WMSD risks. CONCLUSIONS: Based on the findings of this study, it is suggested that SI be further evaluated for its ability to assess WMSD risks associated with non-fixed work tasks. SI presents results that have practical application to non-fixed occupations and differentiate between tasks based on the WMSD risks associated with the tasks.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".