Travel to school patterns and perceptions of walking to school in New Zealand adolescents before versus during the COVID-19 pandemic
Bibliographic record
Abstract
In many countries, adolescents' active school travel rates were declining prior to the onset of the COVID-19 pandemic. Subsequent changes in active school travel have not yet been investigated systematically. This study compared school travel modes and adolescents' perceptions of walking to school 5–6 years before (period 1 (P1)) and 1–2 years after (period 2 (P2)) the onset of the COVID-19 pandemic. Adolescents from Dunedin, New Zealand, completed an online questionnaire about their school travel and perceptions of walking to school in 2014–2015 (P1; n = 1463; 55.2% female) and 2021–2022 (P2; n = 1421; 44.4% female). Home-to-school distance was calculated using Geographic Information Systems. Data analysis included Chi-square tests, logistic regression and ordinary least squares regression. The odds of adolescents walking to school ‘all the time’ or ‘most of the time’ versus ‘never’, ‘sometimes’ or ‘rarely’ were significantly lower in P2 compared with P1. The odds following the pandemic onset were only 0.58 of those pre-pandemic (95% CI: 0.43–0.79), controlling for differences between the two samples. During the same period the proportion of adolescents living in households with ≥2 vehicles increased from 69% to 78%. Adolescents' attitudes towards walking to school indicated significantly lower intentions and higher perceived barriers to walking to school in P2 versus P1, although differences were smaller among those living within walking distance to school. These findings highlight the need for renewed and extended efforts from cross-sectoral actors to support active school transport among adolescents during COVID-19 pandemic recovery efforts and in future similar events.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".