6A.002 Traffic calming implementation around elementary schools: Stepped wedge RCT
Bibliographic record
Abstract
Background Motor-vehicle collisions are a leading cause of child bicyclist and pedestrian injuries in Canada. Injury occurrence and severity are associated with vehicle speeds but may be moderated through traffic calming. As a third of child bicyclist and pedestrian injuries occur within 300 meters of schools, it is important to focus interventions at these locations. This study will compare the effect of two traffic calming measures (i.e., in-street signs and traffic-calming-curbs) around elementary (K-Gr8) schools in Calgary. Methods Using a stepped-wedge cluster randomized controlled trial, 70 eligible elementary schools will be randomly assigned one traffic calming intervention, installed between April and August 2020. Traffic speed and volume (pneumatic tubes), and active transportation prevalence (observational counts), will be collected one week before and one week after intervention installation. Change in outcomes between pre- and post-intervention will be compared within schools for each intervention type. Post-intervention data will also be compared with pre-intervention data from schools yet to receive the intervention. Analyses will include generalized linear mixed effects models. Results Reductions in vehicle speeds are expected for both traffic calming features. Smaller changes in traffic volume and active transportation are expected across all traffic calming features. Greater effects are expected from traffic-calming-curbs. Discussion Scientific evidence on traffic calming intervention effectiveness may improve municipal decision-making, standards for new construction, prioritization of interventions in other jurisdictions, and inform further study in non-school environments. This study is a partnership between the City of Calgary and the University of Calgary.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.010 |
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".