O-433 Which Quebec industries and occupations are at risk of work-related musculoskeletal disorders? A comparison of analyses of 2010–2012 workers’ compensation and 2014–2015 health survey data
Bibliographic record
Abstract
Introduction Non traumatic work-related musculoskeletal disorders (WMSD) represent an enormous burden of preventable illness. Two strategies and data sources to document this burden and identify workers at highest risk were compared. Objectives To identify gender-stratified worker groups at high risk of non-traumatic WMSD by industry and type of occupation and compare WC to health survey results. Methods Using 2014–2015 Quebec Health Survey (QPHS) data on 24,300 workers, measuring self-reported WMSD and industry groupings stratified by occupation (manual/mixed/non-manual), WMSD risk for each industry-occupation group was estimated using gender-stratified adjusted regression analyses and estimation methods. Using Quebec 2010–2012 workers’ compensation (WC) data, gender-stratified WMSD incidence rates per 1,000 full-time equivalent employees (‰ FTEE) were calculated for 174 industry-type-of-occupation groups. WMSD risk was ranked according to Prevention Index scores. Results In both studies, women in manual occupations had the highest WMSD risk compared to male counterparts (WC: 39‰vs27‰ FTEE; QPHS: 36%vs25%); manual male and female workers in administrative/support/cleaning/garbage services were identified at high risk; as well as women in accommodation/restaurant and men in specialised construction trades, civil engineering, and metal manufacturing. Compensation data identified another 9 high-risk groups for men, and 11 for women including 3 health sector groups that ranked in the top 5 for women. Conversely, the QPHS identified another 13 high risk groups in men including several construction and manufacturing sectors and 5 in women. Discussion Differences between the 2 studies’ results are likely due to methodologic differences, including under-reporting in compensation data and the survey’s low power to identify some industries stratified by gender and occupation. Results of the two studies are complementary and each adds to our understanding of which groups are at WMSD risk to target for prevention. Research is needed to compare different survey and compensation data analytic strategies to improve capacity to identify workers at high WMSD risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".