High school sport participation: Does it have an impact on physical activity participation and self-efficacy?
Bibliographic record
Abstract
Inactivity is a known cause of premature deaths worldwide (World Health Organization, 2009; Dwyer et al., 2008). Research has shown that physical activity can prevent diseases, namely cardiovascular disease (PHAC, 2009; Statistics Canada, 2011), diabetes (Warburton et al., 2006; Gregg, Gerzoff, & Caspersen, 2003), and cancer (Canadian Cancer Statistics, 2012). In this study, the association between physical activity participation and self-efficacy for physical activity was measured in adolescent males. The impact of school sport participation on the perception of physical activity was explored, namely the possibility that self-efficacy levels differed between school sport participators and non-school sport participators. Through the use of the Physical Activity Questionnaire-Adolescent (PAQ-A) and the Self-Efficacy for Physical Activity Questionnaire (SEPAQ), a multiple regression was run to determine whether physical activity participation predicted self-efficacy for physical activity, and if either categorical group affected that predictability. PAQ-A scores showed a moderate positive pairwise correlation with SEPAQ score. The results of the Spearman’s p test showed a moderate positive, and significant correlation between PAQ-A and SEPAQ scores, r(113) = .571, p < .01. The regression analysis showed that PAQ-A score significantly predicted SEPAQ scores, b = 10.95, t(113) = 6.63, p < .001. However, school sport participation did not significantly predict SEPAQ scores, b = 0.99, t(113) = 0.97, p > .05. Also, PAQ-A score and school sport participation explained a significant proportion of variance in SEPAQ scores, R^2= 0.33, F (2, 112) = 27.11, p < .001. Implications for male participation in physical activity will be discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".