Active School Travel Intervention Methodologies in North America: A Systematic Review
Bibliographic record
Abstract
CONTEXT: As children's lifestyles have become increasingly sedentary, active school travel can be a relatively accessible way to increase their daily physical activity. In recent years, several different models of interventions have been utilized to promote children participating in active school travel. This review documents and analyzes the different active school travel intervention methodologies that have been used in North America (Canada or U.S.) by collecting, organizing, and evaluating data relating to all phases of active school travel interventions. EVIDENCE ACQUISITION: This systematic review developed a key word search and applied it in six databases (BIOSIS Previews, GeoBase, PubMed, SCOPUS, SPORTDiscus, Web of Science) to gather scholarly literature. A total of 22 studies evaluating children's active school travel interventions in a North American setting (four Canada, 18 U.S.) were identified for the period between January 2010 and March 2017. EVIDENCE SYNTHESIS: Applying the Safe Routes to School Education, Encouragement, Enforcement, Engineering, Equity, and Evaluation ("6 E's") framework, interventions were thematically assessed for their structure and organization, approaches and methods, and outcomes and discussions. Encouragement and education were the most commonly observed themes within the different methodologies of the studies reviewed. Details relating to intervention approaches and methods were common; whereas data relating to intervention structure and organization received much less attention. CONCLUSIONS: Kingdon's multiple streams approach was applied to frame the findings for program facilitators and evaluators. Within the multiple streams approach, several considerations are offered to address and potentially improve active school travel intervention conceptualization, partnerships, organization, and evaluation.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".