A policy analysis exploring hepatitis C risk, prevention, testing, treatment and reinfection within Australia’s prisons
Bibliographic record
Abstract
BACKGROUND: Hepatitis C (HCV) is a global public health concern. There is a global prevalence of 15% among the world's prisoner population, suggesting the need for priority HCV treatment among this population group. New highly efficacious therapies with low side effects, known as directing-acting antivirals, became available under Australia's universal healthcare scheme on 1 March 2016. This creates an opportune time to trial treatment as prevention as an elimination strategy for HCV in prison settings. This paper examines whether policies in Australian jurisdictions support treatment scale-up to achieve elimination among this priority population. METHODS: A comprehensive search was conducted using Google and other web-based search functions to locate all publicly available policies in each Australian state and territory related to HCV health and HCV-related prison health. Ministers (corrections and health) were contacted from each jurisdiction to identify any additional policies. Inductive and deductive analyses were conducted for each jurisdiction, with documents being assessed against a set of four a priori criteria. Documents included in the analysis were current at 1 September 2017, or 18 months following treatment availability. RESULTS: A total of 18 documents were located, including both health (n = 12) and corrections/prison health (n = 6) documents relevant to HCV. Jurisdictions ranged in their commitments for delivering HCV harm reduction strategies and treatment availability within the prison setting. CONCLUSION: Few jurisdictions have updated or published HCV-related health or prisoner health policies following availability of directing-acting antivirals. Current policies do not provide effective support for implementing treatment scale-up that could be possible under universal access to HCV treatment among this priority population.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".