Ethnic, Gender, and BMI Differences in Athletic Identity in Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: Little is known about differences in athletic self-concept that are related to ethnicity, gender, and overweight status, which may influence physical activity behavior. METHODS: Children (N=936) and adolescents (N=1071) completed the Athletic Identity Questionnaire, measuring athletic appearance, competence, importance of activity, and encouragement from parents, teachers, and friends. Multivariate ANOVA assessed group differences and interactions on the 6 subscales. RESULTS: Interaction effects were found in children (Ethnic×Gender; Ethnic×BMI), and ethnic, gender, and BMI (body mass index) main effects in adolescents. In children, Hispanic girls had lower appearance and competence ratings. Within weight categories, normal-weight Hispanic children had lower appearance and importance ratings compared with whites, and obese black children had lower importance ratings than obese whites and Hispanics. In adolescents, there were lower appearance and competence ratings among Hispanics and obese students, lower importance ratings among girls and Hispanics, and less parental encouragement in Hispanics. No gender, ethnic, or BMI differences on encouragement from teachers were found in either children or adolescents. CONCLUSIONS: More negative athletic self-perceptions and less parental encouragement were seen in minorities. Consideration of these factors will be important in interventions to promote physical activity.
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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 | 0.000 | 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) | 0.003 | 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".