Differences in the Correlates of Physical Activity Between Urban and Rural Canadian Youth
Bibliographic record
Abstract
BACKGROUND: Despite the benefits of physical activity (PA), a significant proportion of youth remains inactive. Studies assessing differences in the correlates of PA among urban and rural youth are scarce, and such investigations can help identify subgroups of the population that may need to be targeted for special intervention programs. The purpose of this study was to assess differences in the correlates of PA between Canadian urban and rural youth. METHODS: The sample consisted of 1398 adolescents from 4 urban schools and 1290 adolescents from 4 rural schools. Mean age of the participants was 15.6 +/- 1.3 years. Hierarchical regression analyses were used to examine the association between self-reported PA and a number of demographic, psychological, behavioral, and social correlates. RESULTS: Common correlates between the 2 locations included gender (with girls being less active than boys) perceptions of athletic/physical ability, self-efficacy, interest in organized group activities, use of recreation time, and friends' and siblings' frequency of participation in PA. Active commuting to school and taking a physical education class were unique correlates of PA at the multivariate level in urban and rural students, respectively. Variance explained in PA ranged from 43% for urban school students to 38% for rural school students. CONCLUSIONS: Although more similarities than discrepancies were found in the correlates of PA between the 2 geographical locations, findings from this study strengthen the policies that argue for a coordinated multisector approach to the promotion of PA in youth, which include the family, school, and community.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".