Young workers’ perceptions about occupational carcinogens
Bibliographic record
Abstract
BackgroundYoung workers (≤25 years) face a well-documented increased risk of occupational injury, but little is known about their risk for occupational disease or how it compares to older workers, even though similar factors may contribute to both injuries and hazardous exposures.ObjectiveThe objective of this mixed-methods study was to assess young workers' ability to identify carcinogens and identify factors that may be indicative of a higher risk of occupational cancer.MethodsWe conducted a survey of young workers in Canada and the United Kingdom via Prolific to assess knowledge, attitudes, and behaviours around carcinogenic exposures in the workplace. Participants were asked True/False (T/F) questions on factors affecting hazardous exposures, Likert-scale questions on workplace behaviours, and to identify carcinogens among various hazards. Scores were assigned based on ability to identify carcinogens, median scores were compared across demographics, occupational groupings, and responses. Participants were then recruited to participate in focus groups to discuss questions in further detail.ResultsMedian scores were lowest among participants in (1) retail and sales, and (2) agriculture, trades and manufacturing. Regardless of occupation, the ability to identify carcinogens was low. Median carcinogen scores were lower among incorrect T/F responses related to hazardous exposure. Many participants indicated a lack of knowledge regarding workplace hazards or how they may affect their health despite reporting receiving training.ConclusionsThere are knowledge gaps by occupational groups that highlight a need for improvements to the delivery of training to young workers in the primary sector, manufacturing, and retail and sales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".