Factors and Effects of High School Sport Participation on Young Adult Physical, Mental, and Social Health
Bibliographic record
Abstract
The current physical culture of education and expression for PA is through the medium of sports. However there still remains a gap in the literature for both the self-determined motivation to participate and beneficial consequences of said participation. PURPOSE: to determine predictors of high school participation and effects of high school participation on age 26 behavior, mental health and wellbeing. METHODS: This was accomplished using structural equation models on the longitudinal National Institute for Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD). Most of the sample was female (52%), White (80%) and played organized sports at ages 15 and 18 (51 %). RESULTS: Significant predictors of playing high school sport were: child enjoys PA, parent enjoys PA, parent feels physical education (PE) is important, and device-assessed vigorous PA minutes/week. Playing sports at ages 15 and 18 was associated with age 26 wellbeing, depression, sport participation, and fitness activity participation. CONCLUSION: Sport participation rates are less than a quarter of high school age youth, while participating in sports in high school is associated with better psychological and physical activity behavior outcomes. As a major contributor to our culture's youth physical and character development, sport participation needs to have satisfying outcomes for youth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".