The effectiveness of built environment interventions embedded in road safety policies in urban municipalities in Canada: An environmental scan and scoping review
Bibliographic record
Abstract
Introduction Injuries and deaths from motor vehicle collisions are a significant public health issue. As public health researchers and practitioners, we must support the work of municipalities by advocating for effective interventions to reduce this burden. This requires an evidence-based approach; however, many interventions embedded in existing road safety policies in Canada are not supported by evidence. The objective of this work was to review the built environment (BE) interventions in road safety policies in five, urban municipalities in Canada and summarize the peer-reviewed literature to support them. Methods Data were retrieved through an environmental scan of road safety policies across five Canadian urban municipalities, supplemented by a scoping review of articles indexed in MEDLINE and a grey literature search. Inclusion criteria were: 1) BE interventions, 2) collision or collision pathway outcomes (e.g., vehicle speed, vehicle volume), 3) evaluative study designs, and 4) studies published less than 20 years ago (i.e., 1999–2019). We critically appraised the included studies using the TREND checklist. Data were extracted and summarized, grouped by intervention type. Results The environmental scan yielded 42 BE interventions within the existing road safety policies across CHASE regions. The scoping review found a total of 124 studies; the final sample included 45 studies with 29 interventions. The median TREND score [interquartile range (IQR)] was 16 (15, 17) out of 22. Published scientific evidence was not found for 13interventions. Conclusions A low proportion of included studies specific to the existing road safety policies in urban areas in Canada demonstrated a reduction in collisions. Further, significant variability in the level of effectiveness across interventions exists. Information specific to the effectiveness of interventions should be an integral part of the decision making process for BE change; however, more work is needed to better understand critical decision making factors.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.029 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".