The relationship between the built environment, parental perceptions of traffic safety and walking environment, and children's school travel behaviour in Toronto
Bibliographic record
Abstract
Decline in children’s participation in active school transportation (AST) has drawn attention of those concerned with children’s health and wellbeing. Recent researcher has shown links between built environment, individuals’ psychological and social behaviour, and AST. However, no known research has empirically evaluated their hypothesized relationship simultaneously. This study explored the association between the built environment, parental perceptions of traffic safety and walking environment, sociodemographic characteristics, and AST. Structural equation modeling was employed to quantitatively analyse data on 720 students and their parents, collected from 16 elementary schools in Toronto, Ontario. Findings of this study add new knowledge to the existing literature. Parental perception of the neighbourhood walking environment was found to play a noteworthy role; but the perception of traffic safety had no effect on children’s odds of walking to school. Additionally, dissonance was found between parental subjective views and the objective built environment characteristics. Evidently, AST mode choice is a multilevel and complex process. Suggested improvements include development of new school level programs founded in new City-level children specific 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".