Identifying Influences of Physical Environments and Socio-Demographic Characteristics on a Child's Mode of Travel to and from School.” American Journal of Public Health
Bibliographic record
Abstract
Fewer than half of all children in Canada and the United States are active enough to experience the well-known health benefits of physical activity.1 The most common form of physical activity for people of all ages is walking,2 and for children and youths, the journey to school represents a signif-icant opportunity to increase daily levels of phys-ical activity by using nonmotorized travel modes, such as walking and biking.2–6 Modes of travel to school have changed dramatically over the last 40 years, however, with ever-decreasing use of ‘‘active’ ’ (nonmotorized) travel.7,8 Studies of children’s travel are limited and, in the United States, have found widely varying rates of active travel to school. A study in South Carolina reported that as few as 5 % of ele-mentary school students walked or biked to school,9 and a study of North Carolina children found that 9%walked and 4 % biked.10 Research by Kerr et al.6 based in the Seattle area found that 18 % of students walked or biked to school 5 days a week and 25%used active travel at least 1 day a week. Meanwhile, a comprehensive nationwide study by Martin et al. found that 48 % of students who lived within 1mile of school were active travelers,11 suggesting that geographical factors are at play. We examine sociodemographic and envi-ronmental influences on a child’s mode of travel between home and school in a midsized Canadian city (London, Ontario) and explore differences in travel mode be-tween the journey to school and the trip home from school.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".