Assessing Support for Policy Actions With Co-Benefits for Climate Change and Physical Activity in Canada
Bibliographic record
Abstract
BACKGROUND: Calls to action addressing the interconnections between physical (in)activity and the climate crisis are increasing. The current study aimed to investigate public support for policy actions that potentially have co-benefits for physical activity promotion and climate change mitigation. METHODS: In 2023, a survey through the Angus Reid Forum was completed by 2507 adults living in Canada. Binary logistic regressions were conducted. Separate models were created to reflect support or opposition to the 8 included policy items. Several covariates were included in the models including age, gender, political orientation, physical activity levels, income, urbanicity climate anxiety, and attitudes surrounding physical activity and climate change. The data were weighted to reflect the gender, age, and regional composition of the country. RESULTS: Most individuals living in Canada strongly or moderately supported all actions (ranging from 71% to 85%). Meeting the physical activity guidelines, higher self-reported income, and scoring high on personal experience of climate change were associated with higher odds of supporting the policy actions related to climate actions. CONCLUSIONS: Most adults living in Canada support policies that align with the recommended policy actions related to physical activity and climate change. National campaigns enhancing awareness and understanding of the bidirectional relationship between physical activity and climate change are warranted, and these should consider the consistent demographic differences (eg, gender, age, and political orientation) seen in public support for physical activity-related policies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".