Emergency evacuations during wildfires: a public health perspective in Québec (Canada)
Bibliographic record
Abstract
Purpose The 2023 wildfire season was exceptional in northern Quebec. Due to these wildfires, several towns were evacuated. This study aimed to assess frontline responders’ perspectives on the health and social impacts of major events to improve wildfire preparedness in the region. Design/methodology/approach A preliminary qualitative study was conducted using semi-structured interviews to document the experiences of wildfire evacuation responders from various organizations. A convenience sample of 12 interviewees was recruited using contacts provided by public health authorities. The interview guide, based on intervention evaluation principles, addressed three core themes: structures (e.g. available evacuation resources), processes (services provided during evacuations) and outcomes (health effects). Findings The analysis identified four key stages in the evacuation process that impact health: the evacuation order, transportation, temporary accommodations and the recovery phase. In isolated communities with limited transport networks, evacuating vulnerable populations was challenging, leading to health risks. Displaced individuals stayed in various locations, mostly with family or friends, which fostered social bonds. However, displacement also caused stress, anxiety and distress. During the recovery phase, interviews revealed ongoing psychological and socioeconomic impacts. Interviewees proposed potential solutions for both the healthcare sector and the general population. Originality/value The evacuation process, temporary accommodations and socioeconomic aftermath weakened vulnerable populations, highlighting the need for tailored interventions such as adapted transport and community-based support. Through the analytical lens of vulnerability, the 2023 wildfire season underscored the importance of local context and a holistic approach to public health that considers the cumulative impacts of evacuation, from initial displacement to long-term recovery.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".