Longitudinal Changes in Active Transportation to School in Canadian Youth Aged 6 Through 16 Years
Bibliographic record
Abstract
BACKGROUND: Concern has been raised regarding the increased prevalence of physical inactivity among children. Active transportation, such as walking and cycling to school, is an opportunity for children to be physically active. OBJECTIVE: To identify the sociodemographic predictors of active transportation to schools across time among school-aged children participating in the Canadian National Longitudinal Survey of Children and Youth (NLSCY). METHODS: The sample included 7690 school-aged children attending public schools who were drawn from cycle 2 (1996 and 1997) of the Canadian NLSCY. Data were collected through interviews with the person most knowledgeable about the child. Parents were asked how their child usually gets to school. Responses were dichotomized into active (walking or bicycling) or inactive (school bus, public transit, is driven, or multiple) modes. Using 3 waves of data from the Canadian NLSCY (1996-2001), we estimated the effect of sociodemographic factors on the likelihood of active transportation to school across time using random-effects models. RESULTS: Longitudinal analyses indicated that as children aged, the likelihood of using active transportation to school increased, peaked at the age of 10 years, and then decreased. Urban settings (odds ratio [OR]: 3.66 [95% confidence interval (CI): 3.23-4.15]), households with inadequate income (OR: 1.21 [95% CI: 1.06-1.38]), living with 1 parent (OR: 1.46 [95% CI: 1.29-1.65]), and having an older sibling living at home (OR: 1.14 [95% CI: 1.04-1.25]) were significant predictors of active transportation to school at baseline and carried through across time. CONCLUSIONS: Understanding the factors that influence active transportation may support its adoption by children, which in turn may contribute to meeting physical activity guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".