Assessing the influence of the built environment on physical activity for utility and recreation in suburban metro Vancouver
Bibliographic record
Abstract
BACKGROUND: Physical inactivity and associated co-morbidities such as obesity and cardiovascular disease are estimated to have large societal costs. There is increasing interest in examining the role of the built environment in shaping patterns of physical activity. However, few studies have: (1) simultaneously examined physical activity for leisure and utility; (2) selected study areas with a range of built environment characteristics; and (3) assessed the built environment using high-resolution land use data. METHODS: Data on individuals used for this study are from a survey of 1602 adults in selected sites across suburban Metro Vancouver. Four types of physical activity were assessed: walking to work/school, walking for errands, walking for leisure and moderate physical activity for exercise. The built environment was assessed by constructing one-kilometre road network buffers around each respondent's postal code. Measures of the built environment include terciles of recreational and park land, residential land, institutional land, commercial land and land use mix. RESULTS: Logistic regression analyses showed that walking to work/school and moderate physical activity were not associated with any built environment measure. Living in areas with lower land use mix, lower commercial and lower recreational land increased the odds of low levels of walking for errands. Individuals living in the lower third of land use mix and institutional land were more likely to report low levels of walking for leisure. CONCLUSIONS: These results suggest that walking for errands and leisure have a greater association with the built environment than other dimensions of 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".