Perceptions of the health impacts of climate change among Canadians
Bibliographic record
Abstract
BACKGROUND: 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. The objective of this study was to address this gap by exploring perceptions of the link between climate change and health. METHODS: We conducted a survey of Canadians (n = 3,014) to address this objective. The 116-question survey measured prior consideration 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. ANOVA tests were used to assess differences among sociodemographic groups. RESULTS: Overall, Canadian's have a similar level of concern about health impacts of climate change compared with concern about other impacts (e.g. biophysical, economic, and national security). Among health-related impacts, respondents were more concerned about impacts on water, food and air quality, compared with impacts on mental health, infectious diseases and heat-related illnesses. There were differences among sociodemographic groups; women were significantly more concerned than men about all of the health-related impacts; respondents with a high school level of education were significantly less concerned about all health-related impacts compared with respondents with more education; and respondents on the political left were more concerned with those in the political centre, who were more concerned than those on the political right. CONCLUSION: There is emerging literature suggesting that framing communication around climate change in terms of the health risks it poses may increase perceptions of the proximity of the risks. These results suggest that it is important to be specific in the types of health risks that are communicated, and to consider the concerns of the target sociodemographic groups. 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
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".