Comparison of ergonomic risk assessment output in a repetitive sawmill occupation: Trim-saw operator
Bibliographic record
Abstract
Multiple ergonomic risk assessment methods of unique structure are currently being used to direct industrial prevention initiatives focused on musculoskeletal injuries. In this study, the physical exposures required to perform an at-risk sawmill occupation were collected from 29 subjects via quantified means (surface electromyography and electrogoniometery) and used to calculate several ergonomic risk assessment methods. The aims of this study are to: 1) compare the output of the RULA, REBA, ACGIH TLV, Strain Index and OCRA ergonomic risk assessment methods, 2) examine the assessments' ability to differentiate between facilities reporting meaningfully different incidence rates, and 3) examine the effect of varying the definition of end range posture and exertion required on risk assessment scores. Risk level output assigned by all methods were not sensitive to inter facility differences in risk of injury, suggesting interpretation of risk index and component scores are needed to direct intervention. Components of all methodologies were sensitive to worker technique and facility assessed. Varying variable definition resulted in significantly different component, combined component and/or risk output scores in all methods assessed. The significant effect of posture and exertion variable definition suggests definitions taken to be interchangeable by work site evaluators are not equivalent.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".