Participatory ergonomic processes to reduce musculoskeletal disorders: Summary of a Québec experience
Bibliographic record
Abstract
This article critically reviews 11 participatory ergonomic interventions carried out in Québec by the Occupational Health and Safety Research Institute (IRSST). In the introduction, the characteristics of the approach used are situated in relation to the literature on this subject. Based on the "Ergo team" formula, the approach aims to provide company personnel with the skills to analyze and correct hazardous workstations in relation to musculoskeletal disorders (MSD), using an analysis process that the researchers developed. Although isolated workstations were corrected, the process aims for more general impacts on the company. In the 11 interventions, 40 work situations were analyzed, and in 31 cases, changes were implemented to reduce MSD risks. The most common changes dealt with the tools/equipment (77.4% and physical layouts (84%); changes involving work methods (29% and work organization (12.9%) were less common. The difficulties encountered in the interventions are summarized, and the possible impacts of the interventions on the organization and psychosocial factors are discussed. The authors then address the limitations of the paper and the factors that should be considered in evaluating such a participatory process. The authors conclude that the participatory process was successful in implementing changes in companies and that other studies are necessary for a better understanding of the process and its impacts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".