Supporting children’s participation in active travel: developing an online road safety intervention through a collaborative integrated knowledge translation approach
Bibliographic record
Abstract
Even though regular engagement in physical activity (PA) among children can support their development and encourage the adoption of healthy lifelong habits, most do not achieve their recommended guidelines. Active travel (AT), or any form of human-powered travel (e.g., walking), can be a relatively accessible, manageable, and sustainable way to promote children’s PA. One common barrier to children’s engagement in AT, however, is a reported lack of education and training. To support children’s participation in AT, this paper presents the development of a comprehensive 4-module online road safety education intervention designed to improve children’s knowledge and confidence regarding AT. Using a qualitative integrated knowledge translation (iKT) approach undertaken with community collaborators (n = 50) containing expertise in health promotion, public safety, school administration, and transportation planning, our inductive thematic analysis generated fourth themes which constituted the foundation of the intervention modules: Active Travel Knowledge: Awareness of Benefits and Participation; Pedestrian Safety and Skills: Roles, Responsibilities, and Rules; Signs and Infrastructure: Identification, Literacy, and Behaviour; Wheeling Safety and Skills: Technical Training and Personal Maneuvers. Each theme/module was then linked to an explicit learning objective and connected to complementary knowledge activities, resources, and skill development exercises. Implications for research and practice are discussed.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".