MUSCULOSKELETAL DISORDERS: UNDERSTANDING AND INTERVENTION 149 DECISION AID TOOLS FOR MODIFIED WORK FOR WORKERS WITH MUSCULOSKELETAL DISORDERS
Bibliographic record
Abstract
de recherche Robert-Sauvé en santé et sécurité du travail (IRSST), 5 Université de Montréal, 6 Commission de santé et sécurité du travail (CSST), 7 Association sectorielle paritaire (ASP) Métal-électrique, 8Hydro Québec There is growing evidence that rehabilitation programs that provide modified work to workers with work-related musculoskeletal disorders (WRMD) are more likely to be effective at improving health status and reducing duration of work absence. This presentation described the development of a set of organizational strategies and an approach for implementing workplace-based modified work (MW) programs as well as disorder-specific decision aid tools that facilitate selection of modified work for workers with back pain, neck or shoulder, elbow disorders and hand or wrist disorders. Principles and strategies for modified work and an algorithm of the steps to follow in a Modified Work Program (MWP) were summarized in a guide book. For each of the 4 body regions, questionnaires were designed that allow rapid assessment of the physical demands of proposed modified work tasks by frontline workplace personnel, communication of this information to the treating physician and allow the treating physician to rapidly describe the relevant functional limitations of the injured worker and communicate them to the workplace. All materials have been developed in French and English. An evaluation study of the implementation of the proposed Modified Work intervention and the individual tools is currently underway in 3 metal, electric and electronic companies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".