Québec Law Governing Prevention and Compensation for Work-Related Musculo-Skeletal Disorders (Le Droit Québécois et les Troubles Musculo-Squelettiques: Règles Relatives À L’Indemnisation et la Prévention) (French)
Bibliographic record
Abstract
In Quebec, musculo-skeletal disorders (MSDs) are a significant source of work related disability and constitute the majority of lost time claims for occupational disease. For every MSD compensated as an occupational disease, between 5 and 6 such claims are accepted as resulting from work accidents. Quebec workers’ compensation legislation allows for compensation either as an occupational disease or as a work accident, but in either case this type of claim is frequently the subject of contestation. This article examines the evolution of the legal framework governing access to workers’ compensation for this type of occupational injury and also examines the principle legal mechanisms empowering occupational health and safety (C.S.S.T.) inspectors. The C.S.S.T., is responsible for the implementation of both the workers’ compensation scheme and the prevention mechanisms provided for in the occupational health and safety legislation. The article concludes with a brief survey of recent regulatory developments with regard to ergonomic standards enacted in other Canadian jurisdictions for the purpose of the prevention of MSDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".