Occupational Noise Exposure and Incident Risk of Hypertension Among Construction Workers in the Greater Toronto Area (GTA)
Bibliographic record
Abstract
Occupational noise poses a significant health risk, particularly for blue-collar workers, with potential consequences ranging from hearing loss to cardiovascular diseases. This research addresses the growing concern of hypertension among male construction workers in the Greater Toronto Area (GTA) due to occupational noise pollution, an issue exacerbated by the expected increase in construction industry employment. Hypertension, a major risk for cardiovascular diseases, is a common chronic condition with substantial societal implications. In Ontario, where 7.2% of the workforce is employed in construction, the need to understand and mitigate the impact of noise pollution on hypertension is urgent. This designed study, conducted over a 10-year period, will employ a cohort design, quantitatively measuring noise exposure levels using audio dosimeters by industrial hygienists and categorized into three levels: high, intermediate, and low. Annual physician evaluations will monitor incident cases of hypertension among male construction workers aged 16-65, comparing them to office workers in the same company. Existing research relies heavily on self-reported data and lacks longitudinal monitoring within the construction industry. By employing a rigorous quantitative approach, this study design aims to establish a clear association between occupational noise exposure and hypertension. The implications of the findings extend beyond individual health, impacting families, communities, and public health policies. The study's multidisciplinary approach ensures accessibility to a wide audience, emphasizing its relevance and potential to inform preventive strategies in addressing the global burden of hypertension among blue-collar workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".