A Multisite Study of Environmental Correlates of Active Commuting to School in Mexican Children
Bibliographic record
Abstract
BACKGROUND: Mexican children often use active commuting to school (ACS). In order to maintain high levels of ACS it is important to understand correlates of ACS in this population. However, most evidence comes from high-income countries (HICs). We examined multilevel correlates of ACS in children attending public schools in 3 Mexican cities. METHODS: Information on 1191 children (grades 3 to 5) attending 26 schools was retrieved from questionnaires, neighborhood audits, and geographic information systems data. Multilevel logistic modeling was used to explore individual and environmental correlates of ACS at 400-m and 800-m buffers surrounding schools. RESULTS: Individual positive correlates for ACS included age (6-8 years vs 9-11 years, odds ratio [OR] = 1.5; 6-8 years vs ≥12 years: OR = 2.1) and ≥ 6 adults at home (OR = 2.0). At the 400-m buffer, more ACS was associated with lower walkability (OR = 0.87), presence of posted speed limits (< 6% vs > 12%: OR = 0.36) and crossing aids (< 6% vs 6-20%: OR = 0.25; > 20%: OR = 0.26), as well as higher sidewalk availability (< 70% vs > 90%: OR = 4.5). Similar relationships with speed limits and crossing aids were observed at the 800m buffer. CONCLUSIONS: Findings contrast with those reported in HICs, underscoring the importance of considering the local context when developing strategies to promote ACS. Future studies are needed to replicate these relationships and investigate the longitudinal impact of improving active transportation infrastructure and policies.
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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 | 0.001 |
| 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 | 0.000 |
| 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".