Knowledge and Perceptions of the Health Impacts of Climate Change Among Canadians
Bibliographic record
Abstract
Abstract Background At a time of intersecting public health crises of COVID-19 and climate change, understanding public perceptions of the health risks of climate change is critical to inform risk communication and support the adoption of adaptive behaviours. In Canada, very few studies have explored public understandings and perceptions of climate impacts on health. Methods This study addresses this gap through a nationally-representative survey of Canadians (n=3,014) to explore public perceptions and awareness regarding the link between climate change and health in Canada. The 116-question survey measured awareness of the link between climate change and health, affective assessment of climate health impacts, unprompted knowledge of climate health impacts, and concern about a range of impacts. Kruskal-Wallis tests were used to test for differences in median values among sociodemographic groups. The survey also measured baseline climate opinion, which was used to segment the public into different audiences through a latent class analysis.Results Three climate opinion classes were identified in the sample (disengaged, concerned, and alarmed) and perceptions of climate health impacts were compared across these classes and other sociodemographic variables. Approximately half (53%) of respondents have considerable awareness of the link between climate change and health, and even more (61%) perceive climate change as bad for health. The majority of respondents (58%) can name one or more health impact without prompting. Concern about health impacts of climate change is highest among the alarmed and lowest among the disengaged, as compared to concerns about other categories of climate impacts such as economic. Across the survey, knowledge and concern are highest for water- and food-related health impacts.Conclusions The differential knowledge, awareness, and concern of climate health impacts across segments of the Canadian population can inform targeted communication and engagement to build broader support for adaptation and mitigation measures.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".