Understanding the Associations among Social Vulnerabilities, Indigenous Peoples, and COVID-19 Cases within Canadian Health Regions
Bibliographic record
Abstract
Indigenous Peoples are at an increased risk for infectious disease, including COVID-19, due to the historically embedded deleterious social determinants of health. Furthermore, structural limitations in Canadian federal government data contribute to the lack of comparative rates of COVID-19 between Indigenous and non-Indigenous people. To make visible Indigenous Peoples' experiences in the public health discourse in the midst of COVID-19, this paper aims to answer the following interrelated research questions: (1) What are the associations of key social determinants of health and COVID-19 cases among Canadian health regions? and (2) How do these relationships relate to Indigenous communities? As both proximal and distal social determinants of health conjointly contribute to COVID-19 impacts on Indigenous health, this study used a unique dataset assembled from multiple sources to examine the associations among key social determinants of health characteristics and health with a focus on Indigenous Peoples. We highlight key social vulnerabilities that stem from systemic racism and that place Indigenous populations at increased risk for COVID-19. Many Indigenous health issues are rooted in the historical impacts of colonization, and partially invisible due to systemic federal underfunding in Indigenous communities. The Canadian government must invest in collecting accurate, reliable, and disaggregated data on COVID-19 case counts for Indigenous Peoples, as well as in improving Indigenous community infrastructure and services.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".