Active Commuting to School
Bibliographic record
Abstract
PURPOSE: This study examined the association of objective and perceived neighborhood environmental characteristics and parent concerns with active commuting to school, investigated whether parental concerns varied by environmental characteristics, and compared the association of the perceived environment, parental concerns, and objective environment on the outcome active commuting to school. METHODS: Randomly selected parents of children (aged 5-18 yr), in neighborhoods chosen for their variability in objectively measured walkability and income, completed questionnaires about their neighborhood environment, concerns about children walking to school, and children's behavior (N = 259). Objective measures of the environment were available for each participant and each neighborhood. Logistic regression analyses were used to investigate the relationships among environment, parental concerns, and walking or biking to or from school at least once a week. RESULTS: A parental concerns scale was most strongly associated with child active commuting (odds ratio: 5.2, 95% CI: 2.71-9.96). In high-income neighborhoods, more children actively commuted in high-walkable (34%) than in low-walkable neighborhoods (23%) (odds ratio: 2.1, 95% CI: 1.12-3.97), but no differences were noted in low-income neighborhoods. Parent concerns and neighborhood aesthetics were independently associated with active commuting. Perceived access to local stores and biking or walking facilities accounted for some of the effect of walkability on active commuting. CONCLUSION: Both parent concerns and the built environment were associated with children's active commuting to school. To increase active commuting to school, interventions that include both environmental change and education campaigns may be needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".