Characterizing the intensity of changes made to reduce mechanical exposure
Bibliographic record
Abstract
Interventions to prevent musculoskeletal disorders by reducing mechanical exposures may range from equipment adjustments, through changing workstations and equipment or implementing administrative controls, to the design and redesign of work processes. Although generally positive, the literature reports mixed results for the effects of such workplace interventions on musculoskeletal disorders. We propose that an important factor which influences these results is the change intensity. This construct includes: the body part(s) affected, the size of exposure magnitude reduction in the particular task or tasks involved in the change, the time fraction of the job to which the change applies, the coverage of the change (proportion of the workforce affected), and the adherence (if applicable) by the workforce to the change. The intensities of changes recently completed as part of a participatory ergonomics research program were characterized using this approach. Intensity scores were estimated based upon these parameters for peak and cumulative mechanical exposures. Changes affecting a production system re-design and re-configuration were judged to have medium to high intensity, while most other changes were judged to be of small intensity. Comparisons are made to the intensity of changes determined from reports in the published literature. Factors which maximize intensity as well as potential barriers to achieving higher intensities are described.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".