Dissemination and use of a participatory ergonomics guide for workplaces
Bibliographic record
Abstract
Musculoskeletal disorders (MSDs) result in lost-time injury claims and lost productivity worldwide, placing a substantial burden on workers and workplaces. Participatory ergonomics (PE) is a popular approach to reducing MSDs; however, there are challenges to implementing PE programmes. Using evidence to overcome challenges may be helpful but the impacts of doing so are unknown. We sought to disseminate an evidence-based PE tool and to describe its use. An easy-to-use, evidence-based PE Guide was disseminated to workplace parties, who were surveyed about using the tool. The greatest barrier to using the tool was a lack of time. Reported tool use included for training purposes, sharing and integrating the tool into existing programmes. New actions related to tool use included training, defining team responsibilities and suggesting programme implementation steps. Evidence-based tools could help ergonomists overcome some challenges involved in implementing injury reduction programmes such as PE. Practitioner Summary Practitioners experience challenges implementing programmes to reduce the burden of MSDs in workplaces. Implementing participatory interventions requires multiple workplace parties to be 'on-board'. Disseminating and using evidence-based guides may help to overcome these challenges. Using evidence-based tools may help ergonomics practitioners implement PE programmes.
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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 | 0.126 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".