Implementation contextual factors related to community-based active travel to school interventions: a mixed methods interview study
Bibliographic record
Abstract
BACKGROUND: Active travel to school contributes to multiple physical and psychosocial benefits for youth, yet population rates of active travel to school are alarmingly low in the USA and many other countries. Though walking school bus interventions are effective for increasing rates of active travel to school and children's overall physical activity, uptake of such interventions has been low. The objective of this study was to conduct a mixed methods implementation evaluation to identify contextual factors that serve as barriers and facilitators among existing walking school bus programs. METHODS: Semi-structured interviews guided by the Consolidated Framework for Implementation Research (CFIR) were conducted with leaders of low-sustainability (n = 9) and high-sustainability (n = 11) programs across the USA. A combination of quantitative (CFIR-based) coding and inductive thematic analysis was used. The CFIR-based ratings were compared between the low- and high-sustainability programs and themes, subthemes, and exemplary quotes were provided to summarize the thematic analysis. RESULTS: In both the low- and high-sustainability programs, three of the 15 constructs assessed were commonly rated as positive (i.e., favorable for supporting implementation): student/family needs and resources, implementation climate, and planning. Three constructs were more often rated as positive in the high-sustainability programs: organizational incentives and rewards, engaging students and parents, and reflecting and evaluating. Three constructs were more often rated as positive in the low-sustainability programs: student/family needs and resources - built environment, available resources, and access to knowledge and information. Four themes emerged from the thematic analysis: planning considerations, ongoing coordination considerations, resources and supports, and benefits. CONCLUSIONS: Engagement of students, parents, and community members were among the factors that emerged across the quantitative and qualitative analyses as most critical for supporting walking school bus program implementation. The information provided by program leaders can help in the selection of implementation strategies that overcome known barriers for increasing the long-term success of community-based physical activity interventions such as the walking school bus.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".