Assessing the Intersectionality of Risk Factors and Health Disparities in Canadian COVID-19 Vaccine Allocation
Bibliographic record
Abstract
Due to the COVID-19 global pandemic, the Canadian government initiated multiple mitigation strategies, including the distribution of vaccines to prevent severe disease and hospitalization. Using the Public Health Agency of Canada’s (PHAC’s) Health Equity Approach, we analyze the COVID-19 vaccine campaign in Canada while accounting for frontline healthcare workers, those at higher risk due to preexisting health conditions, older adults, and equity-deserving communities. Equity-deserving communities are defined as groups that experience inequity in healthcare, including First Nations, Inuit, Métis, urban Indigenous (FNIMUI), Black, immigrant, and low socioeconomic status populations. Through evaluation of factors influencing health as depicted by the Health Equity Approach, risk factors such as occupational risks, lack of education, or limitation in access to health resources were identified to contribute to disproportionate COVID-19 infection among these groups. By analyzing past Canadian public health choices in vaccination using the Health Equity Approach, evidence of the disproportionate impacts of COVID-19 suggests the need for generating research-based decisions in improving vaccinations among underserved communities. Knowing that these communities possess a more considerable risk of infection, improving current protocols regarding vaccine accessibility, language barriers, sick leave, and appointment booking is necessary. Conducting qualitative evaluations on the inequities faced by equity-deserving communities can help identify disparities in vaccine prioritization, instigating improvements in resource accessibility when preparing for future variants or pandemics in Canada. Evaluating current partnerships using the Health Equity Approach highlights the need to strengthen collaboration with underserved populations and impose federally regulated policies. By assessing the COVID-19 vaccine campaign in Canada we recommend that future public health campaigns improve program implementation to eliminate disparities in vaccination and infection for underserved communities.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".