A cross-sectional examination of socio-demographic and school-level correlates of children’s school travel mode in Ottawa, Canada
Bibliographic record
Abstract
BACKGROUND: Active school transport (AST) is an important source of children's daily physical activity (PA). However, decreasing rates of AST have been reported in multiple countries during the last decades. The purpose of the present study was to examine the socio-demographic and school-level correlates of AST. METHODS: A stratified sample of children (N = 567, mean age = 10.0 years; 57.8% female) was recruited in the Ottawa area. Four sources of data were used for analyses: 1) child questionnaire including questions on school travel mode and time; 2) parent questionnaire providing information on household socio-demographic characteristics; 3) school administrator survey assessing school policies and practices pertaining to PA; and 4) school site audit performed by the study team. Generalized linear mixed models were used to identify socio-demographic and school-level correlates of AST while controlling for school clustering. RESULTS: Individual factors associated with higher odds of AST were male gender (OR = 1.99; 95% CI = 1.30-3.03), journey time <5 minutes vs. >15 minutes (OR = 2.26; 95% CI = 1.17-4.37), and 5-15 minutes vs. >15 minutes (OR = 2.27; 95% CI = 1.27-4.03). Children were more likely to engage in AST if school administrators reported that crossing guards were employed (OR = 2.29; 95% CI = 1.22-4.30), or if they expressed major or moderate concerns about crime in the school neighbourhood (OR = 3.34; 95% CI = 1.34-8.32). In schools that identified safe routes to school and where traffic calming measures were observed, children were much more likely to engage in AST compared to schools without these features (OR = 7.87; 95% CI = 2.85-21.76). Moreover, if only one of these features was present, this was not associated with an increased likelihood of AST. CONCLUSION: These findings suggest that providing crossing guards may facilitate AST. Additionally, there was a synergy between the identification of safe routes to school and the presence of traffic calming measures, suggesting that these strategies should be used in combination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 teacher head, 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".