Bibliographic record
Abstract
Objective: Understand what factors motivate caregivers’/parents’ decisions of how their children travel to and from school so that policy can be designed to increase Active School Travel (AST).Methods: Follow-up surveys were distributed to five schools in the 2013-2014 school year, and again to three schools in 2014-2015 (22.0% and 40.6% effective response rates). Binomial logistic regression models determined the influence of household variables on caregiver/parental decisions of children’s mode of travel to and from school.Results: Models identified significant effects of accompaniment, distance from home to school, language spoken at home, and perception of neighbourhood safety. Interaction models also identified first-level effects. Conclusions: Caregivers agreed that neighbourhoods were safe, but STP did not increase from AST because STP failed to address moderating attitudinal factors. Interdepartmental/agency coordination with focus on addressing mediating and moderating factors of AST should increase AST.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".