Does parental support influence children's active school travel?
Bibliographic record
Abstract
Today's ‘backseat generation’ of children is more often driven to school. Active school travel (AST) can contribute up to 30% of recommended daily physical activity. Although governed by a complex set of factors, parents are considered ‘gatekeepers’ of children's travel mode decisions. Therefore, we investigate the relationship between parental support and children's AST. Data were from Active Streets , Active People-Junior (British Columbia, Canada). Children self-reported travel mode to/from school for 1 week (10 trips). We assessed parental perceived neighborhood traffic and crime safety (Neighborhood Environmental Walkability Scale-Youth) and frequency of parental support for AST (0–5 ×/week). We investigated the association between daily AST behaviour and parental support using logistic regression (controlling for age, sex, distance to school and perceived neighborhood safety). In our sample ( n = 179, 11.0 ± 1.0 years, 59% girls), 57% reported daily AST and 63% of parents provided daily support. Bivariate analyses showed AST behaviour was significantly associated with parental support frequency and parents' perceived safety. In adjusted analysis, daily parental support remained significantly associated with daily AST (OR 9.0, 95% CI 4.2, 19.7). The relationship between parental support and AST was independent of noted correlates of AST. Thus, interventions that focus solely on changes to the built environment may not be enough to encourage AST. Therefore, interventions that aim to increase AST should involve parents and children in the planning process.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".