Caregiver perceptions of child active transportation safety: a nested pre-post survey
Bibliographic record
Abstract
BACKGROUND: Changes to the urban environment have successfully reduced child injuries and deaths, though their influence on caregiver perceptions of safety and child active transportation (AT) is less understood. This study evaluated whether traffic calming curbs or in-street signs influenced caregiver perceptions of child AT safety. METHODS: Online surveys were emailed to child caregivers before and after intervention installation, including 5-point Likert scale questions on caregivers' perceptions of safety. Participants noted reasons for travel mode and whether/how their child walks/bikes to school. A proportions test compared unmatched responses (those without both presurvey and postsurvey responses), and the Stewart-Maxwell test compared matched responses. Multinomial logistic regression determined characteristics influencing levels of permission to walk/bike to school (restricted, supervised, unsupervised), by intervention. RESULTS: From 52 invited schools, 711 pre-installation and 541 post-installation responses were received (79 matched). Unmatched drivers reported lower proportions of safe ratings for adults at traffic calming curb sites. Unmatched active/public transportation users showed higher proportions of safe responses for traffic speed, traffic volume and road safety. Matched respondents showed increased safety ratings at in-street sign sites for traffic volume safety only. Unmatched respondents reported higher proportions of children walking to school at both locations following installation. Only distance to school at in-street sign locations was associated with walking and bicycling to school. CONCLUSION: Installing built environment features around schools is associated with higher proportions of caregiver perceptions of safety, as well as greater child AT reported prevalence. We recommend prioritising AT user perspectives in traffic-safety planning.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".