Perceived Adverse Health Effects of Heat and Their Determinants in Deprived Neighbourhoods: A Cross-Sectional Survey of Nine Cities in Canada
Bibliographic record
Abstract
This study identifies several characteristics of individuals who report their physical and/or mental health as being adversely affected by summertime heat and humidity, within the most disadvantaged neighbourhoods of the nine largest cities of Québec (Canada). The study is cross-sectional by stratified representative sample; 3485 people were interviewed in their residence. The prevalence of reported impacts was 46%, mostly physical health. Female gender and long-term medical leave are two impact risk indicators in people <65 years of age. Low income and air conditioning at home are risk indicators at all ages. Results for having ≥2 diagnoses of chronic diseases, particularly for people self-describing as in poor health (odds ratio, OR<65 = 5.6; OR≥65 = 4.2), and perceiving daily stress, are independent of age. The prevalence of reported heat-related health impacts is thus very high in those inner cities, with notable differences according to age, stress levels and long-term medical leave, previously unmentioned in the literature. Finally, the total number of pre-existing medical conditions seems to be a preponderant risk factor. This study complements the epidemiologic studies based on mortality or severe morbidity and shows that the heat-related burden of disease appears very important in those communities, affecting several subgroups differentially.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".