L’ergonomie et la réglementation de la prévention des lésions professionnelles en Amérique du Nord
Bibliographic record
Abstract
Cet article examine la réglementation adoptée par cinq administrations publiques en Amérique du Nord qui ont choisi de faire appel à la science de l’ergonomie comme outil réglementaire de prévention des troubles musculo-squelettiques (TMS). Aux États-Unis, seul le règlement de la Californie, d’une portée fort limitée, a pu survivre aux pressions politiques qui ont mené à l’abrogation des règlements de l’État fédéral américain (OSHA) et de l’État de Washington. Au Canada, la Colombie-Britannique et la Saskatchewan appliquent de tels règlements, mais contrairement aux instruments américains abrogés, ceux du Canada misent plutôt sur le processus de prise en charge que sur des normes spécifiques qui quantifient les gestes à risque et déterminent de façon stricte les actions attendues de l’employeur. La description du contexte d’adoption et du contenu des règlements est ensuite suivie d’une comparaison sommaire de cette réglementation avec le droit québécois régissant la prévention des TMS.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".