The Advocacy in Action Study a cluster randomized controlled trial to reduce pedestrian injuries in deprived communities: Figure 1
Bibliographic record
Abstract
BACKGROUND: Road traffic-related injury is a major global public health problem. In most countries, pedestrian injuries occur predominantly to the poorest in society. A number of evaluated interventions are effective in reducing these injuries. Very little research has been carried out into the distribution and determinants of the uptake of these interventions. Previous research has shown an association between local political influence and the distribution of traffic calming after adjustment for historical crash patterns. This led to the hypothesis that advocacy could be used to increase local politicians knowledge of pedestrian injury risk and effective interventions, ultimately resulting in improved pedestrian safety. OBJECTIVE: To design an intervention to improve the uptake of pedestrian safety measures in deprived communities. SETTING: Electoral wards in deprived areas of England and Wales with a poor record of pedestrian safety for children and older adults. METHODS: Design mixedmethods study, incorporating a cluster randomized controlled trial. Data mixture of Geographical Information Systems data collision locations, road safety interventions, telephone interviews, and questionnaires. Randomization 239 electoral wards clustered within 57 local authorities. Participants 615 politicians representing intervention and control wards. Intervention a package of tailored information including maps of pedestrian injuries was designed for intervention politicians, and a general information pack for controls. OUTCOME MEASURES: Primary outcome number of road safety interventions 25 months after randomization. Secondary outcomes politicians interest and involvement in injury prevention cost of interventions. Process evaluation use of advocacy pack, facilitators and barriers to involvement, and success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".