Acceptability of built environment interventions to support active travel in 17 Canadian metropolitan areas: findings from the THEPA study
Bibliographic record
Abstract
evidence on public acceptability can support policy makers in their decision making around which urban interventions to implement.We compare the degree of acceptability for five built environment interventions targeted at active travel and quantify individual-and neighbourhood-level factors associated with greater acceptability of these interventions.We draw on cross-sectional data from the targeting healthy eating and Physical activity (thePa) survey of 27,162 participants living in 17 census Metropolitan areas in canada.Our study focuses on the extent to which participants agreed with: increasing the number of curb extensions at intersections; building protected bicycle lanes; redistributing road space to pedestrians and cyclists; implementing traffic calming measures; and closing street segments to motor vehicles.agreement with interventions ranged from a low of 44.3% for closing streets to a high of 73.5% for increasing the number of curb extensions.across all interventions, people who were younger, women, born outside of canada, had lower incomes, and used alternative modes to driving were more likely to express agreement with implementation of the interventions.there were few associations with neighbourhood-level variables.Our results provide evidence on levels of acceptability of selected transportation interventions in urban canada as well as insight into determinants of greater acceptability.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".