Adoption of Safe Routes to School in Canadian and the United States Contexts: Best Practices and Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it