Multiple work-related accidents: tracing the role of hearing status and noise exposure
Bibliographic record
Abstract
OBJECTIVES: Our main purpose was to investigate any relationship between noise exposure levels in the workplace, degree of hearing loss (HL), and the relative risk of accident (OR of single or multiple events). METHODS: We conducted a retrospective study of 52 982 male workers aged 16-64 years with long-standing exposures to occupational noise over a 5-year period, using "hearing status" and "noise exposure" from the registry held by the Quebec National Institute of Public Health. Information on work-related accidents was obtained from the Quebec Workers' Compensation Board. Hearing threshold level measurements and noise exposures were regressed on the numbers of accidents after adjusting for age. RESULTS: Exposure to extremely noisy environments (L(eq8h) (equivalent noise level for 8 h exposure) > or =90 dBA) is associated with a higher relative risk of accident. The severity of hearing impairment (average bilateral hearing threshold levels at 3, 4 and 6 kHz) increases the relative risk of single and multiple events when threshold levels exceed 15 dB of hearing loss. The relative risk of multiple events (four or more) is approximately three times higher among severely hearing-impaired workers who are exposed to L(eq8h) > or =90 dBA. CONCLUSION: Single and multiple events are associated with high noise exposure and hearing status. This suggests that reducing noise exposure contributes to increased safety in noisy industries and prevents hearing loss. Hearing-impaired workers assigned to noisy workstations should be provided with assistive listening devices and efficient communication strategies should be implemented.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".