Children's mobility, health and happiness: A Canadian school travel planning model
Bibliographic record
Abstract
Active school transport (AST) may be 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. As part of a national initiative, a School Travel Planning intervention is being rolled out to 120 schools across Canada. The objective of this paper is to outline the rationale and implementation of this initiative and present baseline data (n =9217; collected September 2010 to May 2011). After baseline data (parental surveys) are collected, School Travel Plan Facilitators are now working with the schools to assemble a working group of stakeholders (e.g., parents, teachers, traffic engineering professionals) to create and implement an action plan encouraging active transportation choices at each school. At baseline, 37.6% of children walked/cycled to school which increased to 43.8% walking/cycling to home from school in the afternoon. Rates of AST were highest in British Columbia (54.5%) and lowest in Newfoundland (12.5%). Often the target of such initiatives, parents of children (n=1971) who were driven and lived within 1.5 kms from school were most likely to cite convenience (48%) as the reason for driving as opposed to concerns about the safety of their children in terms of traffic (28%) or personal safety (29%). Follow-up data will be collected in September 2011 and will identify whether such barriers can be effectively and feasibly addressed through school travel plans to increase the number of children who walk or cycle to school. Acknowledgments: This research was funded through the Canadian Partnership Against Cancer's 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".