Policymaker Perspectives on the Role of Health Systems in Sustainable Hepatitis C Point‐Of‐Care Testing in Australia
Bibliographic record
Abstract
Point-of-care testing for hepatitis C virus (HCV) offers multiple benefits to key populations and healthcare providers, but it has not achieved widespread implementation. This analysis investigates the impact of the health system on the sustainability of point-of-care HCV testing in Australia. Between September 2023 and January 2024, in-depth, semi-structured interviews were conducted with people involved in HCV policymaking in Australia. Data were coded using WHO's Health System Building Blocks framework (i.e., Health Workforce, Health System Financing, Medical Technologies, Leadership and Governance). Thematic analysis examined how the health system supports and hinders the long-term sustainability of HCV point-of-care testing. There were 29 participants working in seven Australian jurisdictions or nationally: 13 from departments of health, six from community-led organisations, five from local health services, and five from pathology. The analysis demonstrates the interrelations between Building Blocks, but governance was consistently foregrounded across each theme. For Health Workforce, the community approach to models of care in Australia bolstered support for HCV testing outside of traditional healthcare settings. For Health System Financing, sustainability was threatened by a lack of long-term funding mechanisms for point-of-care testing. For Leadership and Governance, state and national HCV elimination targets were seen as important to drive point-of-care testing at the local level, especially when they were reflected in services' key performance indicators. Integration into existing health system structures, sustainable funding mechanisms, and strengthened governance frameworks are needed to sustain HCV point-of-care testing in Australia. Study findings are critical to inform a long-term testing strategy in Australia and internationally.
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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 | 0.038 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".