One Step Forward: Development of a Program Promoting Active School Transportation
Bibliographic record
Abstract
BACKGROUND: Physical activity promotes health and learning. However, up to 80% of the children in industrialized countries do not achieve the recommended level of daily physical activity. By encouraging children to use active school transportation (AST), it is possible to increase their overall physical activity. OBJECTIVE: The aim of this paper was to present the development of an AST intervention using Intervention Mapping (IM) to promote children's physical activity. METHODS: The principles of IM were applied to guide the development of the intervention. The process was divided into 3 phases. First, a literature review and collection of experiences of stakeholders were carried out to gain a broad perspective on the problem and possible solutions. Thereafter, an analysis of the critical environmental and behavioral factors affecting outcome was conducted, which guided the choice of tangible components of the intervention. Finally, a plan of evaluation and implementation was established. RESULTS: A structured program to increase AST among children was developed, consisting of 3 subsequent phases that are described in detail. Implementation took place, and evaluation of the intervention is being carried out. CONCLUSIONS: IM proved to be a valuable method to develop a structured AST intervention for children. By following the steps of the IM process, it became evident that empowerment and gamification are 2 promising avenues to consider when designing AST interventions in a school context. By engaging end users and including important agents, such as parents and teachers, who control the environmental factors, the possibility to design a sustainable program increases. In addition, gamification made it possible to integrate learning into AST, which could motivate schools to devote time and effort to implementing this program.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".