Objectively measured active transportation to school and other destinations among 10–13 year olds
Bibliographic record
Abstract
BACKGROUND: Descriptive data on active transportation in children focuses on the trip to school and has relied on subjective reports. The purpose of this study was to use objective measures to describe total active transportation and active transportation to common destinations within children. METHODS: This was a descriptive study of 388 children aged 10-13 years from Kingston, Ontario, Canada. Participants wore a Garmin GPS watch during waking hours for seven days. Personal Activity Measurement Location System software used the GPS data to identify trips, time spent in each trip and the trip modality (walking, bicycle or vehicle). Google Maps software was used to identify trip destinations. RESULTS: A total of 8875 trips were identified. Most (69%) trips were made by vehicle; 25% were made by walking and 6% by bicycle. Mean time spent in active transportation was 10.3 min/day (95% CI: 7.4, 14.5). Time spent in active transportation was higher for boys (12.1 min/day [95% CI: 8.8, 17.0) than for girls (8.5 min/day [95% CI: 6.1, 12.0]) and increased from 7.7 min/day (95% CI: 5.5, 11.1) at age 10 to 14.3 min/day (95% CI: 10.3, 19.9) at age 13. Time spent in active transportation was lower in the winter by comparison to the other seasons. The four most common active transportation destinations were the participant's home, school, other people's homes, and parks or greenspace with 69%, 39%, 37% and 32% of participants walking or bicycling to these destinations at least once over the 7-day measurement period. CONCLUSION: Over 65% of trips made and time spent travelling occurred in a vehicle. When active transportation was used, the most common destinations were home, school, other people's homes, and parks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".