Epidemiology of Chronic Hepatitis C in First Nations Populations in Canadian Prairie Provinces
Bibliographic record
Abstract
Current structural barriers experienced by First Nations in Canada shape access and engagement for testing and treatment of hepatitis C virus (HCV) infections. This non-systematic informative review considers transdisciplinary perspectives, regional data, and published literature connecting context to the disproportionate HCV burden experienced by First Nations populations in the prairie provinces of Canada, and offers examples of participatory and community-led initiatives working toward the elimination of HCV as a public health threat. First Nations in Canada are disproportionately impacted by chronic HCV infection, with a reported rate of newly diagnosed HCV cases in First Nations communities five times the respective rate in the general Canadian population in 2022. This review explores the reasons underlying the disproportionate burden of HCV infection. Significant over-representation of First Nations in the Canadian Prairies is seen in the major risk categories for HCV acquisition, and the impact of these risk factors is aggravated by barriers to accessing healthcare services and medication coverage. These barriers stem from the legacy of colonialism, discrimination, disenfranchisement, and are exacerbated by stigmatization, victimization, and racism in the justice and healthcare systems. Other contributory factors that impede access to care include inadequate healthcare clinic staffing and infrastructure in First Nations communities, and significant geographical distances between First Nations reserves and laboratories, pharmacies, and treating/prescribing healthcare providers. Recent recognition of historical harms and early steps towards nation-to-nation reconciliation, along with support for culturally connected, holistic, and First Nations-led wellness programs, instill hope that elimination strategies to eradicate HCV infection in First Nations populations will be successful in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".