Exposure to noise and ototoxic chemicals in the Australian workforce
Bibliographic record
Abstract
Objective To determine the current prevalence of exposure to workplace noise and ototoxic chemicals, including co-exposures. Method A cross-sectional telephone survey of nearly 5000 Australian workers was conducted using the web-based application, OccIDEAS. Participants were asked about workplace tasks they performed and predefined algorithms automatically assessed worker’s likelihood of exposure to 10 known ototoxic chemicals as well as estimated their full shift noise exposure level (LAeq,8h) of their most recent working day. Results were extrapolated to represent the Australian working population using a raked weighting technique. Results In the Australian workforce, 19.5% of men and 2.8% of women exceeded the recommended full shift noise limit of 85 dBA during their last working day. Men were more likely to be exposed to noise if they were younger, had trade qualifications and did not live in a major city. Men were more likely exposed to workplace ototoxic chemicals (57.3%) than women (25.3%). Over 80% of workers who exceeded the full shift noise limit were also exposed to at least one ototoxic chemical in their workplace. Conclusion The results demonstrate that exposures to hazardous noise and ototoxic chemicals are widespread in Australian workplaces and co-exposure is common. Occupational exposure occurs predominantly for men and could explain some of the discrepancies in hearing loss prevalence between genders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".