Socioeconomic Factors Influence Physical Activity and Sport in Quebec Schools
Bibliographic record
Abstract
BACKGROUND: School environments providing a wide selection of physical activities and sufficient facilities are both essential and formative to ensure young people adopt active lifestyles. We describe the association between school opportunities for physical activity and socioeconomic factors measured by low-income cutoff index, school size (number of students), and neighborhood population density. METHODS: A cross-sectional survey using a 2-stage stratified sampling method built a representative sample of 143 French-speaking public schools in Quebec, Canada. Self-administered questionnaires collected data describing the physical activities offered and schools' sports facilities. Descriptive and bivariate analyses were performed separately for primary and secondary schools. RESULTS: In primary schools, school size was positively associated with more intramural and extracurricular activities, more diverse interior facilities, and activities promoting active transportation. Low-income primary schools were more likely to offer a single gym. Low-income secondary schools offered lower diversity of intramural activities and fewer exterior sporting facilities. High-income secondary schools with a large school size provided a greater number of opportunities, larger infrastructures, and a wider selection of physical activities than smaller low-income schools. CONCLUSIONS: Results reveal an overall positive association between school availability of physical and sport activity and socioeconomic factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".