Evidence synthesis - Evaluating risk communication during extreme weather and climate change: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Communicating risk to the public continues to be a challenge for public health practitioners working in the area of climate change. We conducted a scoping literature review on the evaluation of risk communication for extreme weather and climate change to inform local public health messaging, consistent with requirements under the Ontario Public Health Standards (OPHS), which were updated in 2018 to include effective communication regarding climate change and extreme weather. METHODS: Search strategies were developed by library information specialists and used to retrieve peer-reviewed academic and grey literature from bibliographic databases (Medline, Embase, Scopus and CINAHL) and Google country specific searches, respectively. The search strategy was validated through a workshop with experts and community stakeholders, with expertise in environment, health, emergency management and risk communication. RESULTS: A total of 43 articles were included. These articles addressed issues such as: climate change (n = 22), flooding (n = 12), hurricane events (n = 5), extreme heat (n = 2), and wild fires (n = 2). Studies were predominantly from the US (n = 14), Europe (n = 6) and Canada (n = 5). CONCLUSION: To meet the OPHS 2018, public health practitioners need to engage in effective risk communication to motivate local actions that mitigate the effects of extreme weather and climate change. Based on the scoping review, risk communication efforts during short-term extreme weather events appear to be more effective than efforts to communicate risk around climate change. This distinction could highlight a unique opportunity for public health to adapt strategies commonly used for extreme weather to climate change.
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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 | 0.060 | 0.305 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".