The Advocacy for Pedestrian Safety Study: Cluster Randomised Trial Evaluating a Political Advocacy Approach to Reduce Pedestrian Injuries in Deprived Communities
Bibliographic record
Abstract
OBJECTIVE: To determine whether advocacy targeted at local politicians leads to action to reduce the risk of pedestrian injury in deprived areas. DESIGN: Cluster randomised controlled trial. SETTING: 239 electoral wards in 57 local authorities in England and Wales. PARTICIPANTS: 617 elected local politicians. INTERVENTIONS: Intervention group politicians were provided with tailored information packs, including maps of casualty sites, numbers injured and a synopsis of effective interventions. MAIN OUTCOME MEASURES: 25-30 months post intervention, primary outcomes included: electoral ward level: percentage of road traffic calmed; proportion with new interventions; school level: percentage with 20 mph zones, Safe Routes to School, pedestrian training or road safety education; politician level: percentage lobbying for safety measures. Secondary outcomes included politicians' interest and involvement in injury prevention, and facilitators and barriers to implementation. RESULTS: PRIMARY OUTCOMES DID NOT SIGNIFICANTLY DIFFER: % difference in traffic calming (0.07, 95%CI: -0.07 to 0.20); proportion of schools with 20 mph zones (RR 1.47, 95%CI: 0.93 to 2.32), Safe Routes to School (RR 1.34, 95%CI: 0.83 to 2.17), pedestrian training (RR 1.23, 95%CI: 0.95 to 1.61) or other safety education (RR 1.16, 95%CI: 0.97 to 1.39). Intervention group politicians reported greater interest in child injury prevention (RR 1.09, 95%CI 1.03 to 1.16), belief in potential to help prevent injuries (RR 1.36, 95%CI 1.16 to 1.61), particularly pedestrian safety (RR 1.55, 95%CI 1.19 to 2.03). 63% of intervention politicians reported supporting new pedestrian safety schemes. The majority found the advocacy information surprising, interesting, effectively presented, and could identify suitable local interventions. CONCLUSIONS: This study demonstrates the feasibility of an innovative approach to translational public health by targeting local politicians in a randomised controlled trial. The intervention package was positively viewed and raised interest but changes in interventions were not statistically significance. Longer term supported advocacy may be needed. TRIAL REGISTRATION: Current Controlled Trials ISRCTN91381117.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".