Seasons of smoke and fire: preparing health systems for improved performance before, during, and after wildfires
Bibliographic record
Abstract
Increased frequency, intensity, and duration of wildfires are intensifying exposure to direct and smoke-related hazards in many areas, leading to evacuation and smoke-related effects on health and health systems that can affect regions extending over thousands of kilometres. Effective preparation and response are currently hampered by inadequate training, continued siloing of disciplines, insufficient finance, and inadequate coordination between health systems and governance at municipal, regional, national, and international levels. This Review highlights the key health and health systems considerations before, during, and after wildfires, and outlines how a health system should respond to optimise population health outcomes now and into the future. The focus is on the implications of wildfires for air quality, mental health, and emergency management, with elements of international policy and finance also addressed. We discuss commonalities of existing climate-resilient health care and disaster management frameworks and integrate them into an approach that addresses issues of financing, leadership and governance, health workforce, health information systems, infrastructure, supply chain, technologies, community interaction and health-care delivery, before, during, and after a wildfire season. This Review is a practical briefing for leaders and health professionals facing severe wildfire seasons and a call to break down silos and join with other disciplines to proactively plan for and fund innovation and coordination in service of a healthier future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".