The right to health: indigenous data sovereignty in Canada during and beyond the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic disproportionately impacted Indigenous Peoples in Canada, highlighting preexisting health inequities. These disparities were exacerbated by inadequate data management policies across Canadian governments, which contribute to inaccurate health information and access challenges for Indigenous Nations. Indigenous data sovereignty, which recognizes the right of Indigenous Peoples to govern their own data, has been identified as essential for achieving self-determination and improving health outcomes. We focus on British Columbia (BC) given its unique health and data governance structure with First Nations. This policy paper examines the challenges related to health data management that arose during COVID-19 in BC, and the regulatory barriers hindering Indigenous health equity. We present four policy recommendations that address data issues as a promising avenue to reducing health inequities in Canada. This includes supporting research by and with Indigenous Peoples, promoting ethical responsibilities of non-Indigenous researchers, implementing anti-racism policies, and adopting Indigenous data management frameworks.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.021 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".