Evaluation of the Canadian School Travel Planning Intervention
Bibliographic record
Abstract
Background: Active school transport (AST) is an important source of children's physical activity (PA). 'School Travel Plans' may increase AST by addressing school-specific concerns such as road safety and traffic congestion. One intervention being implemented across Canada that aims to increase AST is the Canadian School Travel Planning (STP) project. Objective: To evaluate the National STP intervention by examining a) mode shift (pre- to post-intervention), and b) the correlates of mode shift. Methods: Parental surveys were distributed pre- (n=11,243) and post- (n=7304) intervention to gauge family attitudes and practices concerning the school trip. Additionally, each school (n=72) also completed classroom hands-up surveys to determine school travel modes. Results: There was a modest increase (1%) in walking in the morning and afternoon periods. Greater shifts occurred provincially (up to 6%) and on a school-by-school basis (some schools = >20%). 17% of families (1126 households) reported driving less to school, and 18% reported driving less from school. Using binary logistic regression, distance, the child's age and perceptions of neighbourhood safety predicted reductions in driving. Conclusion: The STP intervention achieved its goal of encouraging a National shift from passive to active school travel.Greater shifts towards active school travel may occur once the STP program has had more time to become ingrained in the school and community culture.Acknowledgments: This research was funded through the Canadian Partnership Against CancerÔÇÖs CLASP (Coalitions Linking Action and Science for Prevention) initiative and the Public Health Agency of Canada.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".