The Impact of an After-School Physical Activity Program on Children’s Physical Activity and Well-Being during the COVID-19 Pandemic: A Mixed-Methods Evaluation Study
Bibliographic record
Abstract
Introduction: This study evaluated the impact of the Build Our Kids’ Success (BOKS) after-school program on children’s physical activity (PA) and well-being during the COVID-19 pandemic. Methods: Program leaders, children, and their parents were recruited from after-school programs in Nova Scotia, Canada, that delivered BOKS programming in Fall 2020. After participating, Grade 4–6 children (n = 14) completed the Physical Literacy Assessment for Youth Self (PLAYself), Physical Activity Questionnaire for Older Children (PAQ-C), the Physical Activity Enjoyment Scale (PACES), and 5 National Institutes of Health (NIH) Patient-Reported Outcomes Measures Information System (PROMIS) scales. Children (n = 7), parents (n = 5), and program leaders (n = 3) completed interviews, which were analyzed for themes inductively. Results: The average PAQ-C score was 2.70 ± 0.48, PLAYself was 68.23 ± 13.12, and PACES was 4.22 ± 0.59 (mean ± SD). NIH PROMIS scores were below standard means (cognitive function, family relationships) or within normal limits (peer relationships, positive affect, and life satisfaction). A thematic analysis of interviews revealed that children’s PA levels were impacted by the pandemic and that BOKS positively impacted children’s physical well-being and integrated well with school-based activities. Conclusions: Participation in BOKS provided an overall positive experience and may have mitigated COVID-19-related declines in PA in well-being. The results of this evaluation can inform future physically-active after-school programming.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".