Increasing Children’s Physical Activity
Bibliographic record
Abstract
BACKGROUND: Efforts to increase the prevalence of children's active school transport require evidence to inform the development of comprehensive interventions. This study used a multilevel ecological framework to investigate individual, social, and environmental factors associated with walking to and from school among elementary school-aged children, stratified by gender. METHOD: Boys aged 10 to 13 years (n = 617) and girls aged 9 to 13 years (n = 681) attending 25 Australian primary schools located in high or low walkable neighborhoods completed a 1-week travel diary and a parent/child questionnaire on travel habits and attitudes. RESULTS: Boys were more likely (odds ratio [OR] = 3.37; p < .05) to walk if their school neighborhood had high connectivity and low traffic and less likely to walk if they had to cross a busy road (OR = 0.49; p < .05). For girls, confidence in their ability to walk to or from school without an adult (OR = 2.03), school encouragement (OR = 2.43), scheduling commitments (OR = 0.41), and parent-perceived convenience of driving (OR = 0.24) were significantly associated (p < .05) with walking. Irrespective of gender and proximity to school, child-perceived convenience of walking (boys OR = 2.17 and girls OR = 1.84) and preference to walk to school (child perceived, boys OR = 5.57, girls OR = 1.84 and parent perceived, boys OR = 2.82, girls OR = 1.90) were consistently associated (p < .05) with walking to and from school. CONCLUSION: Although there are gender differences in factors influencing children walking to and from school, proximity to school, the safety of the route, and family time constraints are consistent correlates. These need to be addressed if more children are to be encouraged to walk to and from school.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".