Leveraging built environment interventions to equitably promote health during and after COVID-19 in Toronto, Canada
Bibliographic record
Abstract
A key public health response to the COVID-19 pandemic is the mandate to stay home and practice physical distancing. In Canada, with essential activities such as grocery shopping, outdoor exercise and transportation, people need to be able to safely navigate dense, urban spaces while staying at least two metres or six feet apart. This pandemic has exacerbated the health inequities across neighbourhoods in cities like Toronto, Canada which are often segregated along racial and income lines. These inequities impact who has access to urban infrastructure that promotes health and quality of life. Safety in a time of COVID-19 goes beyond just exposure to the virus, it is complicated by notions of who belongs where, and who has access to what resources. The built environment has a role in maintaining and promoting physical and mental health during this pandemic and beyond it. This paper puts forwards three considerations for built environment interventions to promote health equitably: (i) addressing structural determinants of health and embedding anti-racist intersectional principles, (ii) revisiting tactical urbanism as a health promotion tool and (iii) rethinking community engagement processes through equity-based placemaking. This paper outlines four built environment interventions in Toronto, Canada that seek to address the challenges in navigating urban space safely in the short term, including street design that prioritizes pedestrians, protected cycling infrastructure, access to inclusive green space and safe, affordable housing. Longer-term strategies to create health-promoting urban environments that are equitable are discussed and may be valuable to other cities with similar urban equity concerns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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 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".