Peer-facilitated treatment access for hepatitis C: the Live Hep C Free project
Bibliographic record
Abstract
BACKGROUND: This commentary explores the lessons learned during implementation of a peer-facilitated hepatitis C virus (HCV) testing and treatment access project called the Live Hep C Free (LHCF) project in contributing to micro-elimination efforts. CASE PRESENTATION: The LHCF project aims to facilitate access to on-the-spot HCV testing, treatment, and care in priority settings through partnership between a peer worker (PW) and a clinical nurse. Since the start of the project in January 2018, 4515 people were engaged about HCV and encouraged to access on-site HCV health care, and over 1000 people were screened for HCV and liver health, while almost 250 people accessed HCV treatment through the project. This commentary is intended to prompt discussion about incorporating peer-centred HCV health programs into priority sites. HCV care-delivery models such as the LHCF project can continue to contribute to micro-elimination of HCV in key settings to increase treatment uptake amongst high prevalence and/or marginalised populations and support progress toward national elimination targets. CONCLUSIONS: The LHCF project has been able to highlight the benefits of incorporating trustworthy, efficient, and convenient peer-centred health services to engage and support vulnerable populations through HCV testing and treatment, particularly individuals who have historically been disconnected from the health care system. Additional attention is needed to ensure ongoing funding support to sustain the project and deliver at scale and in expanding evaluation data to examine the operation and outcomes of the project in more detail.
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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 | 0.037 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".