A Global Review of Research on Effective Advocacy and Communication Strategies at the Intersection of Climate Change and Health
Bibliographic record
Abstract
Climate change's intensifying impact on human health necessitates effective advocacy and communication strategies. In this review that spans research in multiple languages published from 2000 to 2023, we summarize evidence on effective advocacy and communication strategies at the intersection of climate change and health for public audiences, health professionals, and public officials. First, we synthesize research on public audiences, including their perceptions of climate change and health, public responses to health-framed climate information, climate and health risks and solutions, information about vulnerable populations and equity considerations, climate- and health-related visual communication and imagery, and their perceptions of health professionals. Then, we provide an overview of research on health professional audiences, including their role in climate and health communication and advocacy, knowledge of the connections between climate and health, willingness to engage with the topic and in climate-relevant actions, and ways to encourage this engagement. Next, we delve into public officials’ perspectives and comprehension of the relationship between climate change and health. Lastly, we end with recommendations for a research agenda to fill the gaps illuminated in this review and foster a growing field on climate and health insight and engagement.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".