Correlates of Participation in Sports and Physical Activities among Indigenous Youth
Bibliographic record
Abstract
Several correlates of Indigenous youth participation in sport and/or physical activities (S/PA) have been recognized; however, there is a paucity of research on the relative importance of these predictors, especially those related to the context in which the youth’s physical activities take place. The purpose of this cross-sectional study is to explore the correlates of participation in S/PA among off-reserve Indigenous youth. Using data from the 2012 Aboriginal Peoples Survey (APS), our analysis was limited to those between the ages of 12 and 17 who were attending elementary or high school and were identified as having a single Indigenous identity (First Nations, Métis, or Inuit [N=4,790]). Using logistic regression, we first assessed unadjusted the effects of each of the correlates on participation in S/PA. We then examined the magnitude of the independent effects of these correlates, controlling for the effects of others. Sampling weights and bootstrap weights were used to account for the multi-stage sampling design employed in the 2012 APS. The results of the multivariate analysis suggest that, controlling for other correlates, youth’s sex, age, health status, drinking behaviour, participation in Indigenous cultural activities and volunteering in community, as well as parental involvement in school activities, strength of family ties, and living in a lone-parent family had statistically significant effects on participation in S/PA. Further research should explore the relationships between these correlates using meditational models to better understand the nature of their effects on participation in S/PA at this age.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".