Sport-based youth development interventions in the United States: a systematic review
Bibliographic record
Abstract
BACKGROUND: The growing number of sport-based youth development interventions provide a potential avenue for integrating sport meaningfully into the U.S. public health agenda. However, efficacy and quality must be reliably established prior to widespread implementation. METHODS: A comprehensive search of databases, peer-reviewed journals, published reviews, and both published and unpublished documents yielded 10,077 distinct records. Title and abstract screening, followed by full-text screening using 6 criteria, resulted in 56 distinct studies (coalescing into 10 sport-based youth development intervention types) included in the synthesis. These studies were then independently assessed and critically appraised. RESULTS: Limited efficacy data were identified, with the quality of methods and evidence largely classified as weak. Processes likely to contribute to the outcomes of sport-based youth development interventions were identified (e.g., predictors of ongoing engagement, alignment between target population and intervention, intervention design), although more rigorous research is needed on these and other processes. Physical health outcomes were only studied in 3 of the 10 intervention types. CONCLUSIONS: The evidence base does not yet warrant wide-scale implementation of sport-based youth development interventions for public health goals within the U. S., although there is promising research that identifies areas for further exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".