Happiness in Motion: Emotions, Well‐Being, and Active School Travel
Bibliographic record
Abstract
BACKGROUND: A pan-Canadian School Travel Planning intervention promoted active school travel (AST). A novel component was exploring emotion, well-being, and travel mode framed by the concept of "sustainable happiness." Relationships between travel mode and emotions, parent perceptions of their child's travel mode on well-being, and factors related to parent perceptions were examined. METHODS: Questionnaires were administered to families (N = 5423) from 76 elementary schools. Explanatory variables were demographics (age and sex), school travel measures (mode, distance, accompaniment by an adult, safety, and barriers), and emotions (parent and child). Outcomes examined parent perceived benefits of travel mode on dimensions of well-being (physical, emotional, community, and environmental). Descriptive statistics, chi-square tests and hierarchical regression were used. RESULTS: Parents and children who used AST reported more positive emotions versus passive travelers. Parents of active travelers reported stronger connections to dimensions of well-being. AST had the strongest association with parents' perceptions of their child's well-being, and positive emotions (parent and child) were also significantly related to well-being on the trip to school. CONCLUSIONS: As an additional potential benefit of AST, interventions should raise awareness of the positive emotional experiences for children and their parents. Future research should experimentally examine if AST causes these emotional benefits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".