Social resiliency in times of crisis: a case study of COVID-19 propensity in a Toronto community
Bibliographic record
Abstract
Abstract Background: This article is intended to advance our understanding of how the intricate planning, distribution, and governance systems and programs in the northwest communities of Toronto have impacted the risk of exposure to the SARS-CoV-2 virus among this population. The study is guided by the research question: how does the conflation of social determinants such as race, healthcare access, housing, and household income impact the COVID-19 infection rate in the northwest neighbourhoods (with the Jane-Finch intersection as the focal point) of Toronto, Ontario? Methods: Using a political economy framework, we consider four social determinants of health—housing, healthcare access, income, and race—and their relationship to the incidence of COVID-19 in five northwest neighbourhoods in Toronto, Ontario with some of the highest COVID-19 case rates. Demographic census data was assessed and compared to social services provided in the city of Toronto’s operating budget. Results: The data analyzed in this study suggest that the lack of investment in social infrastructure exposed residents to an increased likelihood of COVID-19 infection. This inference echoes the work of other fields of research that have described a perpetuation of oppression in which economic growth is prioritized over public health and disease prevention. Conclusion: We contend that when economic opportunities are not afforded to communities, social mobility is stagnant and social resiliency is difficult to achieve. We conclude that in light of the COVID-19 pandemic crisis, the city must rectify its current operating systems and better prepare itself for oncoming crises that may exacerbate further socioeconomic inequalities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".