Interventions for Preventing Residential Fires in Vulnerable Neighbourhoods and Indigenous Communities: A Systematic Review of the Literature
Bibliographic record
Abstract
Globally, residential fires constitute a substantial public health problem, causing major fire-related injury morbidity and mortality. This review examined the literature on residential fire prevention interventions relevant to Indigenous communities and assessed their effectiveness on mitigating fire incidents and their associated human and economic burden. Electronic databases including MEDLINE, EMBASE, CENTRAL, and Web of Science Core Collection were reviewed for studies on fire prevention interventions published after 1990 and based on the 4E's of injury prevention approaches (Education, Enforcement, Engineering, and Engagement). The grey literature and sources including indigenous organizational websites were also searched for eligible studies. Two authors independently screened, selected, and extracted data, in consultation with experts in the field. Outcomes measured included enhanced safety knowledge and practices, decreased residential fires incidents, reduced fire-related injuries and deaths, and lowered costs for healthcare needs. After removing duplicates, screening titles and abstracts, and assessing full texts, 81 articles were included in this review. Of the included studies, 29.1% implemented educational interventions within a variety of settings, including schools, community centres and homes, and included healthcare professionals and firefighters to raise awareness and the acquisition of fire safety skills. Engineering and environmental modifications were adopted in 20.2% of the studies with increased smoke alarm installations being the leading effective intervention followed by sprinkler inspections. Moreover, engagement of household members in hands-on safety training proved to be effective in enhancing household knowledge, fire safety decisions and practices. More importantly, effective outcomes were obtained when multi-faceted fire safety interventions were adopted, e.g., environmental modification and educational interventions, which together markedly reduced fire incidents and associated injuries. This review reveals the dearth of fire prevention evidence gathered directly within Indigenous communities. Nonetheless, relevant fire prevention recommendations can be made, calling for the adoption of combined and context-sensitive fire prevention interventions tailored to targeted Indigenous and vulnerable communities through multiple approaches and measures. Follow-ups and longitudinal studies are critical for accurate evaluation of the long-term outcomes and impacts on preventing residential fires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".