Associations between participation in a Physical Activity‐Based Positive Youth Development Program and Academic Outcomes
Bibliographic record
Abstract
INTRODUCTION: Physical activity-based positive youth development (PYD) programs offer asset building experiences to foster the overall well-being of youth. These programs have the potential to enhance success in other important contexts for children, such as school. However, rigorous examination of this potential impact is needed. METHODS: Propensity score matching was used to compare school outcomes among children who participated in a short, summer physical activity-based PYD program in the USA and children who were from similar backgrounds and from the same school district but did not attend the program. The sample included 149 pairs of students aged 7-12 years (M = 10.11, SD = 1.26) and, in each group, 62% were from ethnically diverse backgrounds, 38% were from primarily Caucasian backgrounds, and 80 were female and 69 were male, and birth years were equally distributed. Ordinal and logistic regression models were used to test for differences between standardized math and language arts test scores, excused and unexcused absences, and total suspensions and expulsions between the two groups. RESULTS: (1, N = 298) = 5.58, p = .02) respectively. No other significant differences were found. When using a more rigorous quasi-experimental and longitudinal design, participation in a PYD program predicted some but not all academic performance and behaviors. PYD programs may need to be designed to specifically nurture academic skills to consistently impact academic outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".