Childhood Sports Participation and Adolescent Sport Profile
Bibliographic record
Abstract
OBJECTIVES: We aimed to increase understanding of the link between sport specialization during childhood and adolescent physical activity (PA). The objectives were as follows: (1) describe the natural course of sport participation over 5 years among children who are early sport samplers or early sport specializers and (2) determine if a sport participation profile in childhood predicts the sport profile in adolescence. METHODS: = 756, ages 10-11 years at study inception) reported their participation in organized and unorganized PA during in-class questionnaires administered every 4 months over 5 years. They were categorized as early sport samplers, early sport specializers, or nonparticipants in year 1 and as recreational sport participants, performance sport participants, or nonparticipants in years 2 to 5. The likelihood that a childhood sport profile would predict the adolescent profile was computed as relative risks. Polynomial logistic regression was used to identify predictors of an adolescent sport profile. RESULTS: Compared with early sport specialization and nonparticipation, early sport sampling in childhood was associated with a higher likelihood of recreational participation (relative risk, 95% confidence interval: 1.55, 1.18-2.03) and a lower likelihood of nonparticipation (0.69, 0.51-0.93) in adolescence. Early sport specialization was associated with a higher likelihood of performance participation (1.65, 1.19-2.28) but not of nonparticipation (1.01, 0.70-1.47) in adolescence. Nonparticipation in childhood was associated with nearly doubling the likelihood of nonparticipation in adolescence (1.88, 1.36-2.62). CONCLUSIONS: Sport sampling should be promoted in childhood because it may be linked to higher PA levels during adolescence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".