Quebec Research on Work-related Musculoskeletal Disorders
Bibliographic record
Abstract
This paper aimed to demonstrate the contribution of the research performed by the musculoskeletal disorders (MSD) Research Axis group of the Quebec Occupational Health and Safety Research Network towards better understanding of work-related MSD (WMSD) development and prevention. Although the group targets its work to the Quebec population, its work has significant impact around the world, placing MSD axis members and knowledge and expertise created in Quebec on the international map. Results from the contribution of members are relevant, primarily in the demonstration of the implication of physical workload as well as organizational and psychosocial work-related factors in the development of WMSDs. Also, members have demonstrated that gender, sex, social class, age and ethno-cultural groups interact in various ways with WMSD determinants. Efforts are devoted towards improving understanding of the physiological responses linked with MSDs, which could also lead to new workplace practices in rehabilitation. The group emphasizes the integration of prevention procedures from the design stage to the workplace intervention. Members have proposed research-based tools for best practices at work and also an original conceptual model as a key, novel element of a theoretical conceptualization of WMSDs. Moreover, the group focuses on improving WMSD surveillance, using multiple sources of information, providing information on newly identified health risks and developing new methods of assessing risk in order to effectively prevent disability in the working population. Finally, results from research of the MSD axis group have implications for orienting legal processes, improving legal recognition of MSDs as occupational diseases, and contributing to the evolution of legal thinking. However, much remains to be done. To that end, the group plans and encourages new initiatives for further advancement. In conclusion, the originality of the work places the group in a favourable position to address the complexity underlying WMSDs, combining expertise that enriches fundamental, clinical and population-based research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".