A Multidimensional Investigation Into the Predictors of Physical Activity in Canadian Adolescents
Bibliographic record
Abstract
BACKGROUND: The percentage of overweight and obese Canadian children and youth is dramatically increasing. Approaches to reducing obesity in adolescents should include the promotion of physical activity (PA) because a continued physically active lifestyle into adulthood may lower rates of chronic diseases associated with unhealthy body weight. PURPOSE: The current study expands on existing assessments of PA to include predictors based in a multidimensional adolescent wellness and ecological model. METHOD: Canadian adolescents (N = 603) were surveyed and the resulting data analyzed using multiple regression analysis. FINDINGS: Overall, 57.5 and 52.9% of the unique variance in PA for females and males, respectively, were explained by the predictors. Significant predictors for females included age, recreational time, family, leadership, and social comparison (cognitive development) skills. For males, equipment at home was also associated with increased PA. CONCLUSIONS: The finding that social comparison and leadership skills are significant predictors of PA in adolescents is new. Nurses should consider a holistic approach to promoting PA whereby these developmental dimensions are included in assessment and prioritized in providing nursing care. Additionally, individualized PA intervention strategies can then be tailored to this unique population.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".