Applying the Consolidated Framework for Implementation Research (CFIR) to examine barriers and facilitators to built environment change in five Canadian municipalities: Lessons from road safety and injury prevention professionals
Bibliographic record
Abstract
Introduction Road traffic injury and death continue to be a concern in Canada. The built environment (BE) is a contributing factor affecting the health of road users, yet there are significant challenges to making injury-reducing BE changes. This research increases our knowledge of these challenges by investigating the opinions of injury prevention and road safety professionals working in Canada about the barriers and facilitators to BE change. Methods Semi-structured interviews and virtual focus groups (VFG) were conducted with 80 key informants (KIs) working in transport and injury prevention sectors in five Canadian cities: Vancouver, Calgary, Peel Region, Toronto, and Montréal. The Consolidated Framework for Implementation Research (CFIR) informed the interview guides. Thematic analysis was used to systematically analyze the data, a process that involved developing codes aligning with the research question, what are the barriers and facilitators to BE change? Themes were cross-referenced with the CFIR domains and constructs to illustrate how barriers and facilitators influence implementation of BE changes. Results The prioritization of motor vehicles, lack of funding and resources, lack of political will, and sectoral silos were described as the most significant barriers to BE change. Cross-sectoral collaboration, data sharing, and champions and advocates were the most significant facilitators. Conclusions Cross-referencing themes with the CFIR situated our findings within the scope of implementation science, which demonstrated how barriers and facilitators influence BE change project implementation. The prioritization of motor vehicles results in disproportionate injury and health risks for VRUs. Collaboration across sectors, with the support of champions and advocates, can facilitate sharing of resources, data, and expertise, which results in more opportunities to enact BE change. Knowledge of these barriers and facilitators, contextualized by the CFIR, makes a case for policy/decision-makers in Canada to approve BE projects that reduce risk of road traffic injury and death.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".