Morbid and Mortal Inequities among Indigenous People in Canada and the United States during the COVID-19 Pandemic Critical Review of Relative Risks and Protections
Bibliographic record
Abstract
The COVID-19 pandemic focused the world’s attention on gross racialized health inequities and injustices. For political and scientific reasons much less is known about the plight of Indigenous peoples than about other ethnic groups. In fact, some of the early pandemic evidence suggested that Indigenous peoples, while clearly experiencing prevalent structural violence probably also experience certain cultural protections. Aiming to begin to clarify their relative risks and protections, we conducted a rapid critical research review and sample-weighted synthesis or meta-analysis of the publishedand gray literature on four COVID-19-relevant outcomes in Canada and the United States between January 1, 2020 and August 1, 2021: vaccination, infection, severe infection, and death rates. Twenty-nine Indigenous-non-Indigenous comparative surveys or cohorts that observed 33, typically age-standardized, incidence or mortality rates or their proxies were included. Consistent with structural violence theory, we found that Indigenous peoples were significantly more likely to be infected, to experience severe COVID-19 illness, or to die as a result of their illness, Indigenous mortal risks (RR = 2.45) being significantly greater than Indigenous morbid risks (RR = 1.40). Consistent with cultural strengths theory, vaccinations seemed equitably distributed (RR = 1.02) with a suggestion of greater vaccine willingness among Indigenous peoples in some places. Clearly, much work remains to be done to decolonize Indigenous research and ultimately practices and policies in North America. Indigenous knowledge user-researcher teams and their allies have much to teach about cultural and ultimately, policy protections.
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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 | 0.033 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".