School travel planning in Canada: Identifying child, family, and school-level characteristics associated with travel mode shift from driving to active school travel
Bibliographic record
Abstract
Objective: Active School Travel (AST) can significantly contribute to children’s physical activity levels. The primary objective of this study was to evaluate the Canadian School Travel Planning (STP) intervention, by examining child, family, and school-level characteristics that are associated with mode shift from driving to AST one year post-intervention. A secondary objective was to highlight which STP strategies were deemed effective by parents of those children who switched travel modes to AST. Methods: Schools (n=103) across Canada participated between January 2010 and March 2012. STP committees implemented strategies to overcome school-specific AST barriers. Mode shift and child/family demographics were assessed by a retrospective, cross-sectional parental survey (n=7827) one year after STP implementation. School level demographics were collected from school administrators. Binomial regression models were applied to examine child, family, and school-level characteristics related to mode shift from driving to AST. Results: Approximately 17% of the sample reported driving less at one-year follow-up both in the morning and afternoon periods. Among these, the majority switched to AST in the morning (n=1002) and afternoon periods (n=995). Results from the regression analyses showed that students in higher elementary grades, living less than 3km from school, attending urban and suburban schools, and attending schools situated in a medium income neighborhood were significantly more likely to change travel mode from driving to AST. Approximately 35% of parents reported that infrastructure improvements and safety education were the most effective STP strategies. Conclusion: The study findings highlight the potential of the STP process in Canada in promoting mode shift from driving to AST. The findings demonstrate STPs may be more effective in some locations where conditions are conducive to mode change. This should inform the development of STP school-selection criteria that may maximize already limited resources by recruiting schools most responsive to STP. Acknowledgments: The intervention and its evaluation were made possible through a financial contribution from Health Canada, through the Canadian Partnership Against Cancer; and from the Public Health Agency of Canada. The views expressed herein represent the views of the Children's Mobility, Health and Happiness: A Canadian School Travel Planning Model and do not necessarily represent the views of the project funders. This research was also supported by the Built Environment, Obesity and Health Strategic Initiative of the Heart and Stroke Foundation of Canada (HSFC) and the Canadian Institutes of Health Research (CIHR).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".