Adoption of Safe Routes to School in Canadian and the United States Contexts: Best Practices and Recommendations
Bibliographic record
Abstract
BACKGROUND: Declines in physical activity (PA) in children and youth have contributed to increases in childhood overweight and obesity. The Safe Routes to School (SRTS) program was developed to promote school active transportation (AT) and reverse the trend. METHODS: Adopting concepts of a realist approach, this article seeks to understand strategies of adoption that worked in the Canadian and United States context. Inclusion criteria consisted of adoption of SRTS program, identification and definition of SRTS, implementation in Canada /United States, and partnership identified. RESULTS: Partnerships focused on increasing the number of children using AT to school. With unique political and funding atmospheres, a common strategy was developing multilevel comprehensive partnerships to mobilize knowledge and resources, as well as to align intervention planning. Key successes, tools used to measure success, as well as benefits, challenges and lessons learned from partnerships were identified. CONCLUSION: This article is the first attempt to examine SRTS at the state/provincial/city level to understand key adoption strategies using a realist approach. It found collaborative community-research partnerships that initiated SRTS and created cultural shifts in communities from the individual to policy level. Researchers, schools and communities interested in increasing school AT should consider SRTS as a valuable approach.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".