A review of COVID-19: Implications for Canadian cities to enhance well-being and resilience
Bibliographic record
Abstract
Among the many detrimental impacts of COVID-19 is diminished well-being. The dimensions of well-being extend beyond a person or household because well-being also pertains to interconnections with society. Canadian cities have been especially hard hit by the pandemic and sustained the brunt of the fallout, but they will recover. The pandemic has heightened awareness of the need for improved urban planning and design for citizen well-being. This paper presents a scoping literature review (2020–1) to portray the impacts and learnings of COVID-19 on cities. The review discusses the impacts the pandemic has had on health and well-being and highlights, for example, the unique vulnerabilities of younger age groups. The findings from the literature review discuss how cities, centres of growth and vibrancy, can improve well-being and resilience. The areas of improvement are categorised in terms of buildings, transport and mobility, green spaces and open areas, and new and expanded digital technologies and artificial intelligence (AI). Then, the recommendations outline proactive governance strategies such as making well-being a strategic priority, meaningful and inclusive citizen engagement and multisectoral collaboration, agile governance and leveraging best practices. The innovations and responsive approaches demonstrated by cities during the pandemic can be redeployed post-pandemic via partnerships to develop sustainable and resilient recovery plans.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".