School-travel by public transit: Rethinking active transportation
Bibliographic record
Abstract
Walking and cycling to school is a source of physical activity (PA). Little is known about public transit use for travel to school and whether it is a physically active alternative to car use for those who live too far to walk. To describe school-trip characteristics, including PA, across travel modes and to assess the association between PA with walk distance. High school students (13.3 ± 0.7 years, 37% female) from Downtown Vancouver wore accelerometers (GT3X +) and global positioning systems (GPS) (QStarz BT-Q1000XT) for 7 days in October 2012. We included students with valid school-trip data (n = 100 trips made by n = 42 students). We manually identified school-trips and mode from GPS and calculated trip duration, distance, speed, and trip-based moderate-to-vigorous PA (MVPA; min). We assessed between-mode differences and associations using multilevel regression analyses (spring 2014). Students accrued 9.1 min (± 5.1) of trip-based MVPA, which was no different between walk and transit trips (p = 0.961). Walking portions of transit trips were similar to walking trips in terms of distance (p = 0.265) and duration (p = 0.493). Walk distance was associated with MVPA in a dose–response manner. Public transit use can contribute meaningfully toward daily PA. Thus, school policies that promote active school-travel should consider including public transit.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".