If You Make it Free, Will They Come? Using a Physical Activity Accessibility Model to Understand the Use of a Free Children’s Recreation Pass
Bibliographic record
Abstract
BACKGROUND: Children's sedentary lifestyles and low physical activity levels may be countered using population-level interventions. This study examines factors influencing the use of a free community-wide physical activity access pass for grade 5 students (G5AP). METHODS: A natural experiment with longitudinal data collection. A sample of 881 children completed the 9-month follow-up survey self-reporting where they used the G5AP. Two analyses were conducted: Getis-Ord GI* geographic cluster analysis of the spatial distribution of users, and logistic regression examining the relationship between use and accessibility (informational, economic, and geographic) and mobility options, while accounting for intrapersonal and interpersonal factors. RESULTS: Overall, 44.9% of children used the G5AP with clusters of high use in urban areas and low use in the suburbs. Other factors significantly related to G5AP included gender (girls), informational accessibility (active recruitment), economic accessibility (median household income), geographic accessibility (facilities within 1.6 km of home), and mobility options (access to Boys & Girls Club bus). CONCLUSIONS: This study found that a diverse population of children used the G5AP. To continue being successful, community-based physical activity interventions need to ensure that the intervention increases geographic, economic, and informational accessibility and provides mobility options that are available to the target population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".