Correlates of Active School Transportation During the COVID-19 Pandemic Among Canadian 7- to 12-Year-Olds: A National Study
Bibliographic record
Abstract
BACKGROUND: Active school transportation (AST) is an important source of physical activity for children and a potentially important climate change mitigation strategy. However, few studies have examined factors associated with AST in the context of the COVID-19 pandemic. METHODS: We used baseline data from a longitudinal survey to investigate correlates of AST during the second wave of COVID-19 (December 2020). We collected survey data from 2291 parents of 7- to 12-year-olds across Canada and linked this information with data on neighborhood walkability and weather from national databases. We assessed potential correlates representing multiple levels of influence of the social-ecological model. We used gender-stratified binary logistic regression models to determine the correlates of children's travel mode to/from school (dichotomized as active vs motorized), while controlling for household income. We examined the correlates of travel mode for both the morning and afternoon trips. RESULTS: Consistent correlates of AST among Canadian children during the COVID-19 pandemic included greater independent mobility, warmer outdoor temperature, having a parent who actively commuted to work or school, living in a household owning fewer vehicles, and living in a more walkable neighborhood. These findings were largely consistent between boys and girls and between morning and afternoon school trips. CONCLUSIONS: Policymakers, urban planners, and public health workers aiming to promote AST should focus on these correlates while ensuring that neighborhoods are safe for children. Future research should monitor the prevalence and correlates of AST as COVID-19 restrictions are removed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".