Public Health Messaging for Wildfire Smoke: Cast a Wide Net
Bibliographic record
Abstract
Wildfire smoke events are increasing in British Columbia (BC), Canada and environmental and public health agencies are responsible for communicating the health-related risks and mitigation strategies. To evaluate and identify opportunities for improving public communications about wildfire smoke and associated health risks we collaborated with end-users and developed a 32-question online survey. The survey was deployed province-wide from 29 September to 31 December 2020 following a severe wildfire smoke episode, which impacted large parts of BC. Using a convenience sample, we disseminated the survey through email lists, radio advertisements, a provincial research platform, and snowball methods. There were 757 respondents, who were generally representative of provincial demographics. Respondents indicated that they receive wildfire smoke messages from diverse sources, including: websites, social media, radio, and television. Radio was identified as the most important source of information for populations that may have increased exposure or health risks, including Indigenous respondents and those working in the trades. Respondents with lower educational attainment expressed that messaging should be simplified. Environmental and public health agencies should continue to share wildfire smoke messages using diverse methods, ideally tailoring the messages and methods to specific populations at risk for exposure and health effects.
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.
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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".