Non-Specialist Treatment Model for Hepatitis C Virus (HCV) in Canadian Carceral Settings: A Telemedical Focus
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a pronounced health problem in carceral settings globally. For Canadian prisons, it is estimated that approximately 25% of those incarcerated have been previously exposed to HCV. Despite being a high prevalence context, Canadian corrections facilities have largely failed to provide adequate care to those with HCV due to their reliance on traditional treatment models. Specifically, this involves hospital-based specialist clinics for patients in corrections facilities nearby – a practice known to be associated with a low incidence of treatment initiation. This paper will explore the use of a contemporary model premised on empowering non-specialist care and the use of telemedicine. This model has found success within other global settings, as will be discussed using case studies from Australia and the United States, and other HCV literature. With the WHO setting an ambitious 90% HCV global reduction goal by 2030, it has become imperative that Canada prioritizes high prevalence populations, such as those in carceral settings, and in turn, looks to more efficient and targeted models of HCV care for these individuals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".