Engaging Community Providers to Create More Active After-School Environments: Results From the Ontario CATCH Kids Club Implementation Project
Bibliographic record
Abstract
BACKGROUND: This paper evaluates the impact of a large-scale, community agency-driven initiative to increase physical activity (PA) in after-school programs in Ontario. In 2008, the YMCA and Boys and Girls Club (BGC) introduced CATCH Kids Club (CKC) into 330 after-school program sites. METHODS: This study assessed the impact of the intervention on the quality and quantity of PA using a pretest/posttest quasi-experimental research design with a comparison non-CKC group. Data were collected at baseline (September 2008) and postintervention (May/June 2009) using the System for Observing Fitness Instruction Time (SOFIT). RESULTS: Nearly all sites, with the exception of the BGC baseline program (a sports program) achieved greater than 50% of time spent in MVPA. Significant differences were not found between levels of MVPA at CKC and comparison sites (59.3% vs. 64.2%), or at CKC sites at baseline versus postintervention (59.3% vs. 52.1%). BGC sites had significantly higher levels MVPA in CKC programs than in sports programs (70.8% vs. 35.2%). In postimplementation interviews, leaders reported general support but some mixed reactions related to how the program was received by participants. CONCLUSIONS: This paper offers support for PA programs that focus on inclusivity and enjoyment and emphasize the important role of staff competency.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".