Association between neighbourhood socioeconomic features and residential fire incidence, related casualties and children: a cross-sectional population-based study in 4 Canadian provinces
Bibliographic record
Abstract
BACKGROUND: This study assessed whether socioeconomic factors affect the rates of residential fire incidence and fire-related injuries and deaths, and whether children are affected differently than the general population. METHODS: We employed a cross-sectional study design using data for British Columbia, Alberta, Manitoba and Ontario from the National Fire Information Database, which includes fire incidents and losses reported by provincial fire marshals across Canada between 2005 and 2015. It also contains 2011 census subdivision social domain data from Statistics Canada based on fire location. Multivariable negative binomial regressions tested the significance of relations between census subdivision socioeconomic factors (average household size, educational attainment, median income and unemployment rate) and the rates of residential fires and casualties per person-year, and casualties per fire incident. RESULTS: Census subdivisions with higher educational attainment and unemployment rates had higher rates of residential fires (incidence rate ratio [IRR] 1.07, 95% confidence interval [CI] 1.05-1.10, and IRR 1.24, 95% CI 1.18-1.31, respectively) and of residential fire casualties per person-year (IRR 1.09, 95% CI 1.05-1.13, and IRR 1.29, 95% CI 1.20-1.40, respectively). Census subdivisions with smaller average households had higher rates of residential fire casualties per person-year (IRR 0.43, 95% CI 0.22-0.83) and per fire incident (IRR 0.75, 95% CI 0.58-0.97), and the association was even stronger for children (IRR 0.17, 95% CI 0.08-0.36, and IRR 0.41, 95% CI 0.20-0.86, respectively). INTERPRETATION: The results suggest that efforts to prevent residential fires should be prioritized in neighbourhoods with higher educational attainment and unemployment, whereas house fire safety programs should be intensified in neighbourhoods with smaller households to prevent fire casualties, especially among children, once a fire does occur.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".