O37-2 Towards understanding relations among social inequalities, gender and working conditions associated with work-related musculoskeletal disorders
Bibliographic record
Abstract
Introduction Reducing health inequalities is a worldwide public health priority. The objectives of this study are to characterise social inequalities related to WMSD-associated work exposures and how they differ by gender/sex. Method Study data were from the 2007–2008 Quebec Survey on Working and Employment Conditions and OHS. Gender/sex stratified multivariable analyses were performed in three steps: 1) logistic regression models to identify work exposures associated with WMSD; 2) calculation of multivariate risk scores (MRS) based on the sum of workers’ exposures weighted by the magnitude of the exposure’s association with WMSD (i.e., its logistic regression coefficient from step 1); 3) linear regression models of the relations between MRS and three measures of socioeconomic status (SES). Results In both genders, WMSD were significantly associated with high physical and quantitative work demands, emotionally demanding work, lack of promotion prospects and unemployment; additionally, in women, WMSD were associated with exposure to sexual harassment, psychological harassment, tense situations with clients, noise, and ≥16 hours computer work/week and, in men, low co-worker support and contradictory work demands. In both genders MRS was significantly associated with lower education and the two lowest socio-occupational classes; in men it was also associated with lower household income and technical occupations and, in women, professional occupations. Discussion The MRS quantified, in a single statistic, the combined work exposures associated with WMSD. Lower occupational classes and educational categories have higher MRS. In women this relationship is more complex, with both less-qualified and professional occupational groups associated with higher MRS. Variations in relations between SES and specific work exposures explain some of these differences. In both genders, the strongest association of MRS was to elementary occupations. Low paid vulnerable workers in such occupations often have less access to adequate OH&S and labour standards protection. These results have implications for targeting preventive interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.023 | 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".