Implications of the COVID-19 pandemic on self-reported health status and noise annoyance in rural and non-rural Canada
Bibliographic record
Abstract
The Canadian Perspectives on Environmental Noise Survey (CPENS), conducted between April 12th, 2021 and May 25th, 2021 coincided with the third wave of the COVID-19 pandemic. Canadians 18 years of age and older (n = 6647) reported the degree to which the pandemic affected their physical health, mental health, stress, annoyance toward environmental and indoor noise, and overall well-being. Depending on the outcome evaluated, between 18 and 67% of respondents reported the measure as "somewhat" or "much worse" due to the pandemic. Stress was most affected, followed by mental health, overall well-being, physical health, annoyance toward environmental noise and annoyance toward indoor noise. Logistic regression models indicated that province, geographic region (rural/remote, suburban, urban), age, gender, poor physical/mental health, heart disease, a history of high sleep disturbance (in general) or diagnosed sleep disorders, anxiety/depression, working/schooling from home, and being retired significantly impacted the odds of reporting a worsening by the pandemic to varying degrees and directions, depending on the outcome. Indigenous status was unrelated to any of the modelled outcomes. Future research could address some of the noted study limitations and provide the data to determine if the observations on the reported measures of health are temporary, or long-lasting.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".