The Association of Rural Elementary School Environmental Characteristics with Children’s Physical Activity Levels at School
Bibliographic record
Abstract
Background: The importance of school settings for obesity prevention efforts may be most critical in low-income rural areas where healthy eating and physical activity (PA) resources are scarce. This study examined the association of rural elementary school environmental characteristics with children’s PA behaviors at school. Methods: Analyses were based on objectively measured height, weight, and PA data from 1443 first to sixth graders attending six rural elementary schools in Oregon. The School Physical Activity and Nutrition Environment Tool (SPAN-ET) was used to measure elementary school PA policy, practice, and physical environments. Multivariable linear regressions were used to examine associations of 29 SPAN-ET PA measurement criteria, with total PA (light, moderate, vigorous; min/d), and moderate-to-vigorous PA (MVPA; min/d), adjusting for child sex, age, and BMI z-score. Results: Our final sample included 755 boys and 688 girls (9 ± 1.7 years); Of them, 16% were overweight and 21% obese. Total PA was positively associated with 21 SPAN-ET PA criteria (unadjusted P value ranged from 0.7 to 0.001; adjusted P < 0.0125); 15 criteria were positively associated with MVPA (unadjusted P value ranged from 0.313 to 0.001; adjusted P < 0.00625). Conclusions: Characteristics of rural school environments are associated with children’s PA behaviors at school. Structured physical education, classroom-based PA, PA messaging, and adequate indoor/outdoor space are important correlates of PA in rural schools.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".