Determinants of Use of Hearing Protection Devices in Canadian Lumber Mill Workers
Bibliographic record
Abstract
BACKGROUND: In a cohort study of lumber mill workers' exposure to noise and incidence of heart disease, initial noise estimates were likely overestimated because they did not account for reductions afforded by the use of hearing protection. As such information was seldom available for individual workers, modeling was necessary to predict hearing protection use and derive adjusted noise measures. OBJECTIVE: To develop a multilevel model of the likelihood of use of hearing protection devices (HPDs) for British Columbia (Canada) lumber mill workers. METHOD: The study population included 13,147 workers in 14 sawmills for whom we had information on HPD use. Subjects self-reported their use of hearing protectors during routine hearing tests over their work history period. Separate multilevel logistic regression models with increasing complexity were developed for a subcohort of workers with complete information (n = 1493) and for a subcohort comprised subjects with hearing tests coinciding with their jobs (n = 10 203). The models included random intercepts for worker and for sawmill. RESULTS: HPD use was associated in both subcohorts with factors such as noise exposure and age. We also showed that specific jobs (such as sawfiling) and departments (planer, in particular) were strongly associated with the use of HPDs. The model illustrates the quantitative importance of including a hierarchical structure which allows for explaining potential sources of outcome variability. CONCLUSIONS: We developed a hierarchical model to predict hearing protection use to enable correction of exposure assessments for use in retrospective epidemiological studies. We showed that this was feasible even in the absence of complete determinant information.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".