The impact of hearing conservation programs on incidence of noise‐Induced hearing loss in Canadian workers
Bibliographic record
Abstract
BACKGROUND: Noise exposure remains one of the most ubiquitous of occupational hazards. Hearing conservation program legislation and the programs themselves were designed to lower risk of resulting occupational noise-induced hearing loss, but there has been no broad-based effort to assess the effectiveness of this policy. METHODS: The incidence of a 10-dB standard threshold shift was examined in a group of Canadian lumber mill workers, using annual audiogram series obtained from the Workers' Compensation Board of British Columbia for the period 1979-1996 and using Cox proportional hazard models. RESULTS: Mean cumulative noise exposure was 98.1 dB-years. The audiograms from 22,376 individuals, among whom there were 2,839 threshold shifts of 10 dB or greater (i.e., a "standard threshold shift"), were retained in multivariable analyses. After adjusting for potential confounders, continuous use of hearing protection, and initial hearing tests later in the study period, the risk for standard threshold shift was reduced by 30%. Risk increased sixfold, however, in those with the highest noise exposure. CONCLUSIONS: Hearing conservation programs may be effective in reducing overall incidence of hearing loss. In the absence of noise control at source, however, highly exposed workers remain at unnecessary risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".