Pedestrian traffic safety and outdoor active play among 10–13 year olds living in a mid-sized city
Bibliographic record
Abstract
This cross-sectional study examined the independent and interactive associations between objective and perceived measures of neighborhood pedestrian traffic safety and outdoor active play. A total of 458 children aged 10–13 years from Kingston, Canada were studied in 2015–2016. Outdoor active play was measured over 7 days using data from activity logs, accelerometers, and Global Positioning System loggers. Geographic Information System data were collected within 1 km of participants' homes and used to create traffic volume, traffic calming, traffic speed, and pedestrian infrastructure indexes. Parents' perceptions of these pedestrian safety domains were obtained by questionnaire. Most of the pedestrian safety measures were not significantly associated with outdoor active play, and there were no interactions between the objective and perceived measures (p > 0.3). The significant relationships are listed here. Children whose parents perceived moderate or high traffic speeds in their neighborhood had outdoor active play values that were 0.35 (SE = 0.10, p = 0.021) and 0.20 (SE = 0.15, p = 0.048) SD units higher, respectively, then children whose parents perceived low traffic speed. By comparison to children from neighborhoods in the lowest tertile, children from the highest traffic volume tertile had higher outdoor active play levels (0.26, SE = 0.12, p = 0.029), while children from neighborhoods in the moderate traffic calming tertile (−0.28, SE = 0.11, p = 0.008) and the moderate pedestrian infrastructure tertile (−0.25, SE = 0.11, p = 0.023) had lower outdoor active play levels.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".