Examining the relationship between human factors related quality risk factors and work related musculoskeletal disorder risk factors in manufacturing
Bibliographic record
Abstract
This study addresses the relationship between human factors (HF) related quality deficits in manufacturing and work-related musculoskeletal disorder (WMSD) risk factors in production staff. A recent systematic review identified 60 HF-related quality risk factors (QRFs) in manufacturing related to product, process and workstation design stages. We investigate the extent to which these identified QRFs are also WMSD risk factors. Each QRF was examined for its relationship with WMSD using a 0 (no relationship) to 10 (strong relationship) scale rubric. The authors rated each QRF separately and then discussed and adjusted their ratings in a review session. Results showed that average median ratings were the highest for QRFs related to product design (8/10), intermediate for QRFs related to workstation design (7/10) and the lowest for QRFs related to process design (5/10). This emphasises the significant role of HF in system design in reducing both quality deficits and risk of developing WMSDs for manufacturing personnel.Practitioner summary: This study investigates whether human-related risk factors for product quality are also risk factors for work-related musculoskeletal disorders in manufacturing. Results showed a substantial relationship between quality risk factors and WMSD risk factors. This indicates the significant role of human factors in operations design in improving both system performance and human wellbeing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".