The political landscape of physical activity and climate action in Canada’s social climate
Bibliographic record
Abstract
Introduction: Political orientation is known to influence health behaviors and policy support. This study examined the implications of political orientation on the social climate of physical activity (PA) and policy support addressing both climate change and PA (i.e., climate-health co-benefits) in Canada. Materials and Methods: Based on the Social Climate Survey 2.0 (18 years+) data, study variables included political orientation (liberal/centrist/conservative), perceived importance of PA, adherence to PA guidelines, causal attributions of physical inactivity, and support for health co-benefits policy actions. Logistic and mediation analyses were performed. Results: Among 1,717 adults, no associations were observed between political orientation and perceived importance of PA or PA guideline adherence. Compared to liberals, both centrists (OR: 1.63, 95 %CI: 1.25-2.08) and conservatives (OR: 1.76, 95 %CI: 1.33-2.32) attributed physical inactivity to individual responsibility. Regarding the endorsement of eight climate-health co-benefits policy actions, centrists (ORs: 0.53-0.66) and conservatives were less likely to support such actions (ORs: 0.27-0.48), compared to liberals, which were partially explained by the attribution of causes for physical inactivity, among conservatives only. Discussions: Most Canadian adults recognized the importance of PA. Support for climate-health co-benefit policies varied by political orientation, with centrists showing mixed support-favoring cost-effective policy actions. Causal beliefs appear to be associated with lower support for policy actions among conservatives. Conclusion: Tailoring public health messages, policy advocacy strategies, and investment priorities to align with varying political orientations may be important for addressing climate-health co-benefits and its broader public health implications in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".