Active Transportation Safety Features around Schools in Canada
Bibliographic record
Abstract
The purpose of this study was to describe the presence and quality of active transportation safety features in Canadian school environments that relate to pedestrian and bicycle safety. Variations in these features and associated traffic concerns as perceived by school administrators were examined by geographic status and school type. The study was based on schools that participated in 2009/2010 Health Behaviour in School-aged Children (HBSC) survey. ArcGIS software version 10 and Google Earth were used to assess the presence and quality of ten different active transportation safety features. Findings suggest that there are crosswalks and good sidewalk coverage in the environments surrounding most Canadian schools, but a dearth of bicycle lanes and other traffic calming measures (e.g., speed bumps, traffic chokers). Significant urban/rural inequities exist with a greater prevalence of sidewalk coverage, crosswalks, traffic medians, and speed bumps in urban areas. With the exception of bicycle lanes, the active transportation safety features that were present were generally rated as high quality. Traffic was more of a concern to administrators in urban areas. This study provides novel information about active transportation safety features in Canadian school environments. This information could help guide public health efforts aimed at increasing active transportation levels while simultaneously decreasing active transportation injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".