165 Creating a community fire safety and prevention toolkit with the national indigenous fire safety council, Canada
Bibliographic record
Abstract
<h3>Background</h3> Indigenous communities bear a disproportionate burden of residential fire-related deaths and injuries compared to non-indigenous counterparts, posing a threat to community health and well-being. Lack of access to fire safety and prevention resources and effective programs contribute to the increased risk of fire-related injuries and deaths. <h3>Objective</h3> To develop a community resource for Indigenous and small communities interested in reducing the frequency and severity of fire incidents, and their associated injuries and deaths. <h3>Methods</h3> Development of this resource was based upon previous work to review fire safety and prevention evidence and practices, and to document the process of gathering information regarding Indigenous community needs concerning fire safety knowledge and research needs. Strategies were developed, contextualized, and refined though consultation and input from experts in the field of fire and injury prevention. An evaluation of the National Indigenous Fires Safety Council (NIFSC) community fire safety education programming assessed the evidence supporting each program. A level of evidence (LOE) score was based upon the amount available evidence and study designs, using a summary 5-star system to indicate the overall quality and level of evidence. <h3>Results</h3> The LEAD Fire Safety and Prevention Community Toolkit is a step-by-step workbook built upon four main strategies: Learn about community characteristics and fire burden; Engage with community members and build support; Assess available resources and identify opportunities; and Develop and implement fire safety and prevention action plan. It leads users through a process from learning about the community fire burden, to raising community support, to planning evidence-based and data-driven fire safety and prevention activities, to implementing the action plan and evaluating the results. The leading NIFSC programs promoted in the toolkit are the Home Safety Assessment, Smoke Alarm and Carbon Monoxide Installation, and Home Escape Planning. A set of indicators is included to document baseline community fire burden and prevention activities, and to evaluate the implementation, short-term outcomes, and long-term impact of the fire safety and prevention action plan. <h3>Conclusions</h3> This toolkit guides Indigenous and small communities to use best practices in reducing the frequency and severity of fire; those that are evidence-based, cost-effective, and efficient.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".