A scoping review of wildfire smoke risk communications: issues, gaps, and recommendations
Bibliographic record
Abstract
BACKGROUND: Wildfire smoke exposure has become a growing public health concern, as megafires and fires at the wildland urban interface increase in incidence and severity. Smoke contains many pollutants that negatively impact health and is linked to a number of health complications and chronic diseases. Communicating effectively with the public, especially at-risk populations, to reduce their exposure to this environmental pollutant has become a public health priority. Although wildfire smoke risk communication research has also increased in the past decade, best practice guidance is limited, and most health communications do not adhere to health literacy principles: readability, accessibility, and actionability. This scoping review identifies peer-reviewed studies about wildfire smoke risk communications to identify gaps in research and evaluation of communications and programs that seek to educate the public. METHODS: Four hundred fifty-one articles were identified from Web of Science and PubMed databases. After screening, 21 articles were included in the final sample for the abstraction process and qualitative thematic analysis. Ten articles were based in the US, with the other half in Australia, Canada, Italy, and other countries. Fifteen articles examined communication materials and messaging recommendations. Eight papers described communication delivery strategies. Eleven articles discussed behavior change. Six articles touched on risk communications for vulnerable populations; findings were limited and called for increasing awareness and prioritizing risk communications for at-risk populations. RESULTS: This scoping review found limited studies describing behavior change to reduce wildfire smoke exposure, characteristics of effective communication materials and messaging, and communication delivery strategies. Literature on risk communications, dissemination, and behavior change for vulnerable populations was even more limited. CONCLUSIONS: Recommendations include providing risk communications that are easy-to-understand and adapted to specific needs of at-risk groups. Communications should provide a limited number of messages that include specific actions for avoiding smoke exposure. Effective communications should use mixed media formats and a wide variety of dissemination strategies. There is a pressing need for more intervention research and effectiveness evaluation of risk communications about wildfire smoke exposure, and more development and dissemination of risk communications for both the general public and vulnerable populations.
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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 | 0.025 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".