Built environment change for injury prevention: insights from public health professionals across public health units in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Road-related injuries and deaths are among the most significant and avoidable public health problems in Canada. Modifications to the built environment (BE) can reduce injury rates for vulnerable road users (VRUs) and other priority populations who experience disproportionate risk. This paper highlights public health professionals' experiences working in injury prevention across Ontario public health units (PHUs) navigating barriers and facilitators to BE change. Their perspectives offer valuable insights that can support future BE change work in the Ontario public health context, thus illustrating the importance of including practitioners' voices in injury prevention research. METHODS: Qualitative data were collected for a larger pan-Canadian study examining barriers and facilitators to BE change from the perspectives of injury prevention and transport professionals working across a variety of sectors, including public health, using key informant interviews (KI) and virtual focus groups (VFGs). Participants (n = 9) from four PHUs are included in this present study: Peel Region; York Region; Peterborough; and Ottawa. Thematic analysis was used to organize and code the data in relation to the guiding principles of the Ontario Public Health Standards (OPHS), situating our results within the broader context of public health and road-related injury prevention in Ontario. RESULTS: Major barriers included motor vehicle prioritization and decision-making structures. Facilitators included partnerships and collaboration, champions and advocates, and access to data. Lastly, participants highlighted the important role of public health in BE change discussions and decision-making for road-related injury prevention. CONCLUSION: Public health professionals' insights about barriers and facilitators show that some of their work aligns with the existing OPHS. Needs of local populations are clearly identified, while local data illustrating the impact of public health interventions are lacking. There are limits to PHU's capacities, as well as the capacities of communities, which can be strengthened through the work of champions and advocates. Partnerships, collaboration, and engagement are also significant facilitators to BE change, aligning with the OPHS, but PHUs need to be involved more in BE change processes in order to prioritize the safety needs of VRUs in local communities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".