Contextualizing Canada’s hepatitis C virus epidemic
Bibliographic record
Abstract
In 2016, Canada signed on to the World Health Organization (WHO) 2030 hepatitis C virus (HCV) disease elimination targets. Most of Canada's HCV disease burden is among five disproportionately affected population groups: 1) Baby boomers, who are at increased risk of dying from decompensated cirrhosis and hepatocellular carcinoma and for whom one-time screening should be recommended to identify those undiagnosed; 2) People who inject drugs (PWID), whose mortality risks include HCV infection, HCV acquisition risks and co-morbid conditions. While HCV infection in PWID can be effectively cured with direct-acting antivirals, premature deaths from acquisition risks, now exacerbated by Canada's opioid crisis, will need to be addressed to achieve the full benefits of curative treatment. PWID require syndemic-based solutions (harm reduction, addictions and mental health support, and management of co-infections, including HIV); 3) Indigenous populations who will require wellness-based health promotion, prevention, care and treatment designed by Indigenous people to address their underlying health disparities; 4) Immigrants who will require culturally designed and linguistically appropriate services to enhance screening and engagement into care; and (5) For those incarcerated because of drug-related crimes, decriminalization and better access to harm reduction could help reduce the impact of HCV infections and premature mortality. A comprehensive prevention, care and treatment framework is needed for Canada's vulnerable populations, including those co-infected with HIV, if we are to achieve the WHO HCV elimination targets by 2030. The aim of this review is to describe the HCV epidemic in the Canadian context.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".