Motivations for Adolescent Participation in Leisure-Time Physical Activity: International Differences
Bibliographic record
Abstract
BACKGROUND: Although there are substantial international differences in adolescent physical activity (PA), cross-country motivational differences have received limited attention, perhaps due to the lack of measures applicable internationally. METHODS: Identical self-report measures assessing PA and motivations for PA were used to survey students ages 11, 13, and 15 from 7 countries participating in the 2005-2006 Health Behavior in School-Aged Children (HBSC) study representing 3 regions: Eastern Europe, Western Europe and North America. Multigroup comparisons with Confirmatory Factor Analysis and Structural Equation Modeling examined the stability of factors across regions and regional differences in relations between PA and motives for PA. RESULTS: Three PA motivation factors were identified as suitable for assessing international populations. There were significant regional, gender, and age differences in relations between PA and each of the 3 PA motives. Social and achievement motives were positively related to PA. However, the association of PA with health motivations varied significantly by region and gender. The patterns suggest the importance of social motives for PA and the possibility that health may not be a reliable motivator for adolescent PA. CONCLUSION: Programs to increase PA in adolescence need to determine which motives are effective for the particular population being targeted.
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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 | 0.004 |
| 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 | 0.000 |
| 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".