Perspective: Young Workers at Higher Risk for Carcinogen Exposures
Bibliographic record
Abstract
Young workers, those under the age of 25, are considered a vulnerable working population, primarily due to their increased risk of injury. In this study we investigate if young workers may also be at an increased risk for occupational exposure to carcinogens. Using the 2006 and 2016 Canadian Census of Population and previously obtained CAREX Canada data, this study aimed to identify sectors and occupations that have high proportions of young workers and where potential exists for exposure to known and suspected carcinogens. Key groups where young workers are likely at a higher risk for occupational exposure to carcinogens were identified. Our work shows that young workers in construction, outdoor occupations, and farming are key groups that warrant further investigation. These specific groups are highlighted because of the large number of young workers employed in these sectors/situations, the high number of possible carcinogen exposures, and the potential for higher risk behavior patterns that typically occur in these types of jobs. While there is no data available to develop carcinogen exposure estimates specific to young workers, it is our perspective that young workers are likely at a higher risk for occupational exposure to carcinogens. Our findings identify opportunities to improve the occupational health and safety for this vulnerable population, particularly for young construction workers, farm workers, and outdoor 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".