Do socio-economic determinants affect residential fire-related injuries and deaths among Canadian children?
Bibliographic record
Abstract
Abstract BACKGROUND Fire is a leading cause of unintentional injuries among children in Canada. More specifically, residential fires are responsible for too many preventable deaths and injuries. Yet, there is no national database that reports on residential fire-related injuries and deaths among children. Although socio-economic determinants (SED) have been associated with increased risk of residential fire in the USA and UK, little is known about the significance of this impact on the Canadian child population. OBJECTIVES This study examined the role of SED (low education, median income and average number of persons per dwelling (ANPD)) in residential fire-related injuries and deaths, and assessed the relationship between age and the severity of residential fire-related injuries and deaths, among children and youth in Canada. DESIGN/METHODS A cross-sectional study design was used to examine data from the National Fire Information Database (NFID), which includes 10-years (2005–2015) of microdata information on fire incidents and losses reported by provincial/territorial Fire Marshals and Fire Commissioners Offices across Canada. Census 2011 data at the CSD level, from Statistics Canada, provided the SED variables. Our outcome of interest was the odds of death and major injury over minor injury. A logistic regression model was applied to test the relationship between age and SED with our outcome of interest, while adjusting for province. RESULTS For every 1 person increase in the average ANPD at the CSD level, there is a 31% decrease in the odds of dying or being severely injured in a residential fire (p=0.0003). For every 1% increase in CSD’s low education proportion, there is a 2.5% increase in the odds of dying or being severely injured in a residential fire (p=0.0002). Median income was not significantly associated with the odds of death and major injury over minor injury. The odds of death and major injury were not significantly different for youths and adults, compared to children, controlling for ANDP, low education and median income. CONCLUSION The National Fire Information Database is one of the first to amass reliable fire incident and loss information across Canada into one database. Using this novel dataset, we determined that increased ANPD strongly decreased the odds of death and major injury over minor injury. Thus, the number of persons living in a household should be considered when targeting vulnerable children and youth for residential fire prevention and safety promotion programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".