Public Engagement with Climate Change and Health: A Global Literature Review
Bibliographic record
Abstract
With the impacts of climate change on health becoming increasingly severe and far-reaching, effective communication to diverse audiences is more crucial than ever. This review analyzes 93 studies published between 2000 and 2023 on public understanding and responses to information about climate change and health. We synthesize research on public perceptions of climate change and health, responses to health-framed climate information, and information about climate and health risks and solutions, and the depolarizing potential of health messaging. Our findings suggest that conveying the health relevance of climate change holds significant potential for enhancing public engagement and building support for climate action. Additionally, we identify research gaps, particularly in understanding how different demographic audiences perceive health-related climate information and suggest directions for future studies. This synthesis of international research provides valuable insights into how different populations perceive and react to health-related climate information, highlighting the importance of targeted and effective communication strategies in addressing the climate crisis. The findings and summaries in this review can serve as valuable tools for evidence-based initiatives to address the critical issue of climate change and its profound implications for public health.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".