Prevalence of occupational exposure to carcinogens among workers of Arabic, Chinese and Vietnamese ancestry in Australia
Bibliographic record
Abstract
BACKGROUND: Although job-related diseases result in more deaths per year than job-related injuries, most research concerning ethnic minority workers has concerned accidents and injuries rather than disease-causing exposures such as carcinogens. METHODS: We conducted a telephone-based cross-sectional survey to estimate the prevalence of occupational exposure to carcinogens among a sample of ethnic minority workers in Australia, and compared their exposure prevalence to that of a sample of the general Australian-born working population ('Australian workers'). RESULTS: One-third of the ethnic minority workers were exposed to at least one carcinogen at work. The likelihood of exposure to carcinogens was not significantly different from that of Australian workers, although the likelihood of exposure to individual carcinogens varied by ethnicity. CONCLUSION: Knowing the prevalence of exposure to carcinogens in the workplace in different ethnic groups will allow better targeted and informed occupational health and safety measures to be implemented where necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".