Active Commuting to School in Mexican Adolescents: Evidence From the Mexican National Nutrition and Health Survey
Bibliographic record
Abstract
BACKGROUND: Travel to school offers a convenient way to increase physical activity (PA) levels in youth. We examined the prevalence and correlates of active commuting to school (ACS) in a nationally representative sample of Mexican adolescents. A secondary objective was to explore the association between ACS and BMI status. METHODS: Using data of adolescents (10-14 years old) from the 2012 Mexican National Health and Nutrition Survey (n = 2952) we ran multivariate regression models to explore the correlates of ACS and to test the association between ACS and BMI z-score or overweight/obesity. Models were adjusted for potential confounders and design effect. RESULTS: 70.8% of adolescents engaged in ACS (walking: 68.8%, bicycling: 2.0%). ACS was negatively associated with travel time, age, mother's education level, household motor vehicle ownership, family socioeconomic status, and living in urban areas or the North region of the country (P < .05). Time in ACS was negatively associated with overweight/obesity: Each additional minute of ACS was associated with a 1% decrease in the odds for being overweight or obese (P < .05). CONCLUSIONS: Potential correlates of ACS that may result in benefits for Mexican adolescents are identified. More studies on this relationship are needed to develop interventions aimed at increasing PA through ACS in Mexico.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".