Prescribing direct-acting antivirals for hepatitis C treatment: a scoping review of factors that influence primary care providers
Bibliographic record
Abstract
BACKGROUND: Hepatitis C is a significant public health challenge in Australia, particularly in diagnosis, treatment access, and ongoing care among people who inject drugs. Despite the availability of highly effective direct-acting antivirals and government subsidisation, treatment uptake has declined among this population in recent years, beyond what would be expected from the initial treatment of easier-to-reach patients. OBJECTIVES: This rapid scoping review aimed to identify barriers and enablers affecting primary care providers in prescribing direct-acting antivirals for hepatitis C treatment. ELIGIBILITY CRITERIA: Studies were included if they: were published after 2014, focused on DAA treatment, included primary care provider perspectives, contained primary data, identified barriers/enablers to treatment, and were conducted in high-income countries. SOURCES OF EVIDENCE: Two databases (Web of Science and Google Scholar) were searched for peer-reviewed articles. Primary care stakeholders were consulted through an online survey (n = 10) and telephone interviews (n = 7) to contextualise and validate findings. CHARTING METHODS: Data were charted using a standardised form capturing author, year, location, aim, participants, study details, and main findings. Analysis used a deductive approach to identify key themes. RESULTS: Twenty-three articles, mostly quantitative studies, were included in the review. The analysis identified four key domains influencing direct-acting antiviral prescription: provider characteristics, healthcare systems and service delivery, models of care, and societal and structural issues. CONCLUSIONS: This review provides insights into contemporary challenges in hepatitis C care delivery models and highlights critical structural, sociocultural, and interpersonal factors affecting testing and treatment, particularly for people who inject drugs. These findings have implications for improving direct-acting antiviral prescription rates in primary care settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".