Public health communication strategies during wildfire events: Lessons for the United States from a global perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wildfires are an escalating environmental and public health threat in the United States, driven by climate change, prolonged droughts, and urban expansion into wildfire-prone areas. These events produce severe health hazards through smoke exposure, with impacts ranging from respiratory illnesses and cardiovascular complications to mental health challenges. Public health communication is a crucial tool for mitigating these risks, shaping protective behaviors, and promoting community resilience. This research article surveys public health communication strategies employed during wildfire events globally and evaluates their relevance to the U.S. context. Lessons from countries such as Australia and Canada reveal innovative approaches to engaging diverse populations, leveraging technology, and addressing inequities in information access. The discussion highlights persistent challenges in the United States, including disparities in communication reach, the spread of misinformation, and variable levels of public trust. Recommendations emphasize the need for multi-channel, culturally tailored, and community-centered strategies to strengthen U.S. public health communication capacity in the face of worsening wildfire crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it