Innovation and employee injury risk in automotive disassembly operations
Bibliographic record
Abstract
Engineering innovations in car disassembly systems are studied for affects on system operators’ risk of repetitive strain injury (RSI). Objective instrumented measures of injury risk factors with synchronised video-based task analyses were used to examine changes in operators’ RSI risk during two cases of engineering innovation: (1) a shift in industrial model from traditional extracting saleable parts to line-based full material recovery, and (2) the prospective effects of a simulated ‘Lean’-inspired process improvement in the line system. Both cases of innovation showed significantly increased movement speeds and reduced muscular recovery opportunities, implying increased RSI risk. This case study reveals a mechanism by which innovation may increase RSI risks for operators. Managers responsible for engineering innovation should ensure their teams have the tools and mandate necessary to control injury hazards as part of the development and design process. These cases suggest how failure to manage RSI hazards in the innovation process may allow increases of injury risks that can compromise operational performance. This ‘innovation pitfall’ has implications for operator health and organisational sustainability. Alternative pathways are discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".