A comparison of self-reported health status and perceptual responses toward environmental noise in rural, suburban, and urban regions in Canada
Bibliographic record
Abstract
Health Canada, in collaboration with Advanis, conducted the Canadian Perspectives on Environmental Noise Survey (CPENS) to investigate expectations and attitudes toward environmental noise in rural and non-rural Canada. The CPENS, a 26-item questionnaire, was completed online by 6647 randomly selected Canadians, age 18 y and older between April and May 2021. The prevalence of reporting their area as often or always calm, quiet, and relaxing was 76.8%, 64%, and 48.4% in rural/remote, suburban, and urban, respectively. A high expectation of quiet was less prevalent yet followed the same pattern: rural/remote (58.2%), suburban (37.4%), and urban (21.8%). Self-reported health status and noise sensitivity were unrelated to geographic region. A high magnitude of non-specific sleep disturbance over the previous 12 months was reported by 7.8% overall; highest among urban dwellers (9.8%), followed by suburban (7.2%) and rural/remote (5.5%) dwellers (p < 0.01). High annoyance toward road traffic noise was 8.5% overall, and significantly higher in urban (10.5%), relative to suburban (7.9%) and rural/remote (6.6%) areas (p < 0.0001). Annoyance toward noise from rail, aircraft, mining, industry, marine activity, construction, wind turbines, and landscaping equipment is reported. The analysis also explores potential differences between Indigenous Peoples of Canada and non-Indigenous Canadians in their attitudes and expectations toward environmental noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".