Hepatitis C Diagnosis and Treatment Among Indigenous People in a Canadian Context: Challenges and Community-Led Solutions
Bibliographic record
Abstract
The historical and ongoing impacts of the influence of colonization are experienced by Indigenous people in systemic racism, inequity in healthcare access, and intergenerational trauma; originating in the disruption of a way of life and seen in a grief response, with links to disparate hepatitis C virus (HCV) prevalence. Despite this, the focus often remains on the increased incidence without a strengths-based lens. Although HCV is a global concern that can result in cirrhosis, liver failure, or cancer, diagnosing and linking people to care and treatment early can prevent advanced liver disease. Efforts to engage certain priority populations are occurring; however, historical context and current practices are often forgotten or overlooked. This is especially true with respect to Indigenous people in Canada. This review considers the published literature to elucidate the context of historical and ongoing colonizing impacts seen in the current HCV treatment gaps experienced by Indigenous people in Canada. In addition, we highlight strengths-based and Indigenous-led initiatives and programming that inspire hopefulness and steps toward community-engaged solutions to meet the World Health Organization Goals of eliminating HCV as a public health threat.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".