Overrepresentation of New Workers in Jobs with Multiple Carcinogen Exposures in Canada
Bibliographic record
Abstract
Background. In Canada, understanding the demographic and job-related factors influencing the prevalence of new workers and their exposure to potential carcinogens is crucial for improving workplace safety and guiding policy interventions. Methods. Logistic regression was performed on the 2017 Labour Force Survey (LFS), to estimate the likelihood of being a new worker based on age, industry, occupation, season, and immigration status. Participants were categorized by sector and occupation using the North American Industry Classification System (NAICS) 2017 Version 1.0 and National Occupational Classification (NOC) system 2016 Version 1.0. Finally, an exposures-per-worker metric was used to highlight the hazardous exposures new workers encounter in their jobs and industries. Results. Individuals younger than 25 years had 3.24 times the odds of being new workers compared to those in the 25–39 age group (adjusted odds ratios (OR) = 3.24, 95% confidence interval (95% CI) = 3.18, 3.31). Recent immigrants (less than 10 years in the country) were more likely to be new workers than those with Canadian citizenship (OR 1.36, 95% CI: 1.32, 1.41). The total workforce exposures-per-worker metric using CAREX Canada data was 0.56. By occupation, new workers were the most overrepresented in jobs in natural resources and agriculture (20.5% new workers), where they also experienced a high exposures-per-worker metric (1.57). Conclusions. Younger workers (under 25 years) and recent immigrants who had arrived 10 or fewer years prior were more likely to be new workers, and were overrepresented in jobs with more frequent hazardous exposures (Construction, Agriculture, and Trades).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".