Evaluating the implementation of active transportation infrastructure during the pandemic: a RE-AIM study
Bibliographic record
Abstract
BACKGROUND: The impact of urban transportation on human health is a critical area of study in public health. The COVID-19 pandemic created a need to re-imagine urban public spaces and trial built environment interventions under expedited timelines and physical distancing requirements. By expanding street space for active transportation during the pandemic, cities intended to enable access to essential destinations through ‘street reallocation’ interventions, with potential implications on physical and mental health. Using the RE-AIM framework, our aim is to evaluate the COVID-19 pandemic street reallocations in the cities of Vancouver, Toronto, and Montréal in Canada. METHODS: Using a mixed methods approach, we evaluated street reallocation implementation across RE-AIM dimensions (Reach, Effectiveness, Adoption, Implementation, and Maintenance). We conducted a socio-spatial analysis using publicly available data, including cycling infrastructure, area-level sociodemographic characteristics, and measures of accessibility to destinations. To identify barriers and enablers of street reallocation implementation and to add nuance to the socio-spatial analysis, we conducted semi-structured interviews with municipal practitioners in 2022. We coded and analyzed the data using framework analysis. Reach: In Vancouver, Toronto, and Montréal, 39.9%, 21.0%, and 32.9% of the population had access to street reallocations in their census tract. Effectiveness: In Vancouver, most street reallocations were in areas with high accessibility to (i.e., spatial proximity to) employment, and in Toronto and Montréal in areas with high accessibility to parks. Adoption: The majority of street reallocations were implemented near pre-existing cycling infrastructure. Though not targeted as users of the street reallocations, local business owners were important in the decision-making process. Implementation: A facilitator of street reallocation implementation was communication between municipal practitioners and community members, and continuous feedback cycles. Maintenance: Temporary materials used to initially construct street reallocations were high maintenance which resulted in high costs. Sustainability of street reallocation interventions hinges on community support. CONCLUSIONS: Our findings help to inform the work of municipal practitioners, public health professionals, and researchers looking to build healthier and more equitable transportation systems. A focus of future implementation science research may be on developing consistent metrics for built environment intervention evaluation.
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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 | 0.056 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".