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 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 | 0.022 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".