S-85 Sex and gender inequalities: segregation of OHS exposures and preventive avenues for a population of low-educated teenagers entering the labour force
Bibliographic record
Abstract
As men and women hold different jobs, have different social roles and different power influence in societal strata, they are exposed to different physical and psychosocial hazards at work. Adolescents with lower levels of education who start working are particularly vulnerable to sex/gender segregation of tasks and exposure to different work hazards. In Quebec, adolescents who have experienced significant academic delays are referred to the Work-Oriented Training Program (WOTP). In this program, they learn semi-skilled trades by doing practicums, through Co-operative Education. These placements involve many occupational health and safety risks. For example, students may be exposed to various toxic substances in cleaning jobs, to wood or metal dust in processing plants or garages, to allergens in pet care businesses, hair salons, or child care centers. As jobs are segregated by gender, prevention approaches must take this into account. Occupational health and safety (OHS) risk factors may differ by sex and/or gender (e.g. when a small girl uses tools designed for tall men; or when manual handling training only considers ‘boxes’ as potential loads rather than a variety of situations, such as angry children or objects). Accordingly, occupational safety and health programs and prevention strategies should consider sex and gender-related factors. Our team is developing educational tools and resources to help these students improve and maintain their health as they enter the workforce, through an equity perspective. This presentation will discuss the differentiated hazards faced by low-educated male and female adolescents, as well as promising prevention avenues, including specific consideration for ‘invisible risks’ often encountered by young women. The WOTP examples that will be given can be applicable to other contexts of vocational training and work integration in low-skilled jobs for young workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".