563 Fire safety in homeless encampments
Bibliographic record
Abstract
Abstract Introduction It is well known that people experiencing homelessness are at a greater risk for burn injury. Our burn centre saw an increase in admissions of homeless individuals during the pandemic. Typically, we partner with our hospital’s communications staff to share burn prevention public service announcements. But our usual method of broadcasting information through media like newspapers, blog posts, Facebook, or Instagram was not necessarily going to reach people sleeping rough. This report describes the development of a partnership between a burn centre, outreach workers, and people with lived experience of homelessness to improve fire safety in encampments. Methods Our goal was to create a Fire Safety Manual and hold Fire Safety Training Sessions. We conducted surveys that asked encampment residents questions like, “What do you use fires for?” “What fire hazards do you see at encampments?” and “How do you think fires could best be prevented?”. We used the results of this survey to guide the training manual and held workshops to engage encampment residents and incorporate feedback into the manual. Results The manual uses harm reductions strategies and focuses on real-life situations encountered by folks living outdoors—the manual outlines how to safely start a fire and what to do if a fire occurs. The reality is that people are trying to survive freezing winters while sleeping outside; this means that some safety standards are not possible, and the guide had to reflect that. For example, we practiced fire escape plans during training sessions and had to think about obstacles like tents with only one way out. A solution was to keep a utility knife inside and outside the tent in case one had to cut through to escape or free someone. An encampment resident suggested hiding the knives so they would not be used as weapons. We purchased fire extinguishers, fire blankets, and first aid kits that we distributed during training. Conclusions Education is critical to prevent burn injuries. Burn centre staff may be experts on burn prevention, but we are not experts on surviving outside. We have to be accountable to this community. This means listening, building trust, and partnering with people living outdoors. People who did training sessions were empowered to start fire brigades in their encampments. Crucial concepts are to meet people where they are and always to include people with lived experience: “Nothing about us without us.”
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".