A Health Equity Lens Contributes to an Effective Pandemic Response: A Canadian Regional Perspective
Bibliographic record
Abstract
As cases of COVID-19 began to increase in Ontario, Canada, throughout 2020, early evidence from surveillance and media highlighted disproportionately higher rates of COVID-19 infection, hospitalization and mortality among racialized and low-income populations. This disproportionate impact on underserved populations calls for a shift in approach away from what has traditionally occurred in health protection, that is the use of a universal approach which assumes everyone is affected and benefits equally from the same type and intensity of interventions. In this article, public health agencies are, therefore, being called to consider moving away from using a purely universal approach, often used in the control of communicable diseases, and apply a more tailored approach and use principles of health equity and proportionate universalism to reduce COVID-19 cases and their impacts among underserved groups and address health inequities exacerbated by the pandemic. We highlight examples from York Region Public Health, one of the largest health units in Ontario, to demonstrate areas of possible impact of this paradigm shift. It is clear that with a health equity lens applied to the pandemic response, the impact of COVID-19 can be further reduced and health inequities that predated the global pandemic can improve.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".