Using a modified stepped-wedge randomized controlled trial to evaluate effectiveness of elementary school traffic calming interventions
Bibliographic record
Abstract
Background Collisions with motor-vehicles are a leading cause of severe child bicyclist and pedestrian injuries in Canada. Injury rates and severity are associated with traffic speeds and volume but are moderated through traffic calming. Objective To assess the association of specific traffic calming measures on changes in traffic speed and volume, and active transportation prevalence, around Calgary, Alberta, Canada elementary schools, both immediately following installation and months later. Methods A uniquely modified stepped wedge randomized controlled trial was conducted at a sample of 52 public elementary schools, from July 2020 to May 2021. Outcomes included traffic speed, traffic volume, and active transportation counts. Data were collected pre- and post-installation for all schools, and up to 32 weeks post-installation at select schools. Results In-street signs were associated with a reduction of between 0.53 and 0.94 km/h (on average) in traffic speeds, depending on time-period. Traffic calming curbs were associated with a reduction of 0.80 km/h during the morning time-period. In-street signs were associated with a reduction of active transportation prevalence in the morning, and an increase in the afternoon. Conclusions Small reductions in traffic speed and volume were observed at locations with in-street signs, with potential reductions to child active transportation injury risk. The use of a modified stepped wedge trial design to evaluate traffic calming features is unique and may guide further evaluation of built environment effects on health outcomes. Studies should assess whether expanding to roadways with higher speed limits may lead to greater reductions in speed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".