Examining How Neighborhood Socioeconomic Status, Geographic Accessibility, and Informational Accessibility Influence the Uptake of a Free Population-Level Physical Activity Intervention for Children
Bibliographic record
Abstract
PURPOSE: To evaluate the uptake of ACT-i-Pass (G5AP), a physical activity (PA) intervention that provides free access to PA opportunities, and to understand the extent to which the intervention provides equitable access to children. DESIGN: This study evaluates the differences in uptake (ie, enrollment) by comparing postal codes of registrants with the postal codes of all eligible children. SETTING: Children were provided the opportunity to register for the G5AP during the 2014 to 2015 school year in London, Canada. PARTICIPANTS: The population of grade 5 students in London who registered for the G5AP (n = 1484) and did not register (n = 1589). INTERVENTION: The G5AP offered grade 5 students free access to select PA facilities/programs during 2014 to 2015 school year. MEASURES: Measures included G5AP registration status, method of recruitment, distance between home and the nearest facility, and neighborhood socioeconomic status. ANALYSIS: Getis-Ord Gi* and multilevel logistic regression were used to analyze these data. RESULTS: There were significant differences in the uptake of the G5AP: residing in neighborhoods of high income (odds ratio [OR] = 1.062, P = .029) and high proportion of recent immigrants (OR = 1.036, P = .001) increased the likelihood of G5AP registration. Children who were recruited actively were significantly more likely to register for the G5AP (OR = 2.444, P < .001). CONCLUSION: To increase the uptake of a PA intervention, children need to be actively recruited. Interactive presentations provide children with increased access to information about both the program and its nuances that cannot be communicated as effectively through passive methods.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".