Evacuate or shelter-in-place? Applying a risk-informed decision support tool for long-term care facilities threatened by wildfire
Bibliographic record
Abstract
The summer of 2023 was Canada's most destructive wildfire season in recorded history. The southern region of the province of British Columbia (BC) is prone to wildfires and flooding, placing infrastructure, communities and human lives at risk. Residents of long-term care (LTC) facilities are especially vulnerable to these events. Healthcare leaders face the challenge of deciding when and under what circumstances to evacuate an LTC facility. This requires careful evaluation of the dangers posed by the event and the risks associated with the sudden displacement of frail residents. This risk assessment leads to two decision points: is it safer for residents to shelter-in-place or to evacuate to an alternative care facility? Given the increasing frequency and severity of climate-related disasters and their impact on the health and well-being of LTC residents, health emergency incident managers identified the need to develop a standardised approach for evacuation decision making. This paper analyses how the Interior Health (IH) Authority collaborated with Health Emergency Management BC (HEMBC) to develop an Evacuation Risk Decision-Support Tool. This tool informed LTC facility evacuations during the 2023 McDougall Creek wildfire in West Kelowna, BC.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".