The ACT-i-Pass study protocol: How does free access to recreation opportunities impact children’s physical activity levels?
Bibliographic record
Abstract
BACKGROUND: Physical activity during childhood is associated with a multitude of physical, behavioural, and psychological health benefits. Identification of effective population level strategies for increasing children's physical activity levels is critical for improving the overall health of Canadians. The overall objective of this study is to assess how a naturally-occurring, community-level intervention which offers Grade 5 children in London, Canada a free access pass to physical activity opportunities (facilities and programs) for an entire school year can lead to increased physical activity among recipients. METHODS/DESIGN: This study adopts a longitudinal cohort study design to assess the effectiveness of improving children's access to physical activity opportunities for increasing their physical activity levels. To meet our overall objective we have three aims: (1) to assess whether the provision of free access increases children's physical activity levels during and after the intervention compared to a control group; (2) to assess how and why child-specific trajectories of physical activity (between-children differences in level of physical activity measured across time) in the intervention group differ according to children's individual and household characteristics; and (3) to explore additional factors that are unaccounted for in the theoretical model to gain a further understanding of why the free access intervention had varying effects on changing physical activity levels. We will be addressing these aims using a mixed methods approach, including: a series of youth surveys conducted before, during, immediately after, and 4-months after the intervention; parent surveys before, during, and post-intervention; real-time tracking of the access pass use during the intervention; and focus groups at the conclusion of the intervention. Data compiled from the youth surveys will provide a subjective measure of physical activity to be used as our outcome measure to address our primary aims. DISCUSSION: The results of this study can inform policy- and decision-makers about the sub-groups of the population that benefitted the most (or least) from the intervention to provide more specific information on how to develop and target future interventions to have a greater impact on the physical activity levels and overall health of children.
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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 | 0.045 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.014 |
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".