In the eyes of Indigenous people in Canada: exposing the underlying colonial etiology of hepatitis C and the imperative for trauma-informed care
Bibliographic record
Abstract
Background: The distribution of hepatitis C (HCV) infection in Canada signals a widening gap between Indigenous and non-Indigenous people. Current evidence demonstrates that the rate of HCV infection among Indigenous people is at least five times higher than the rest of Canada. This analysis provides a reconciliatory response, which exposes the colonial etiology of the HCV gap in Canada and proposes potential anti-colonial approaches to HCV wellness and health care for Indigenous people. Methods: This analysis applies Two-Eyed Seeing as a reconciliatory methodology to advance the understanding of HCV burden and identify the key elements of responsive HCV care in the context of Indigenous nations in Canada. Results: The analysis underlines the colonial distribution of HCV burden in Canada, highlights Indigenous perspectives on HCV infection, hypothesizes a clinical pathway for the underlying colonial etiology of HCV infection, and identifies Indigenous healing as a promising anti-colonial conceptual approach to HCV wellness and health care among Indigenous people. Conclusions: In the eyes of Indigenous people, HCV infection is a colonial illness that entails healing as an anti-colonial approach to achieving wellness and gaining health. Future empirical research should elaborate on the colonial HCV pathway hypothesis and inform the development of a framework for HCV healing among Indigenous people in Canada.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".