Does Climate Matter? How Temperature and Precipitation Affect the Relationship between Neighbourhood Walkability and Walking for Transportation
Bibliographic record
Abstract
Previous research has found associations between neighbourhood walkability and transport walking, which has health benefits. However, the influence of climate on these associations is poorly understood. Our objective is to examine how climate modifies the association between walkability and walking to work or school across Canada. We hypothesize that this association is weaker in areas with more extreme climates.The study population is from the 2007-2014 Canadian Community Health Surveys, ongoing cross-sectional surveys of Canadians aged 12 and up. We excluded people who lived in rural areas or did not work or attend school. These surveys collect self-reported data on socio-demographics and transport walking. Climate data are 1981-2010 climate normals from weather stations across Canada and walkability data are Walk Score® values of respondents’ residential locations. We estimated associations between walkability (split into quintiles) and energy expenditure on walking to work/school, adjusting for socio-demographics. To examine how climate modifies this association, we will incorporate the following variables into these models: mean temperature in hottest and coldest months, mean precipitation in wettest and driest months, days per year and degree days over and under specific temperatures.The mean energy expenditure on walking to work/school was 0.26 kcal/kg/day among people in the highest walkability quintile, compared to 0.08 kcal/kg/day in the lowest quintile. After adjusting for socio-demographics, the difference in energy expenditure was 0.17 kcal/kg/day (95% confidence interval [0.15-0.18]). Climate variables will be incorporated into these models, with results forthcoming.On average, people living in more walkable neighbourhoods walk to work or school more than people in less walkable neighbourhoods. Incorporation of climate variables into this analysis will inform whether climate should be considered when evaluating walkability and physical activity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".