Interventions to Improve Uptake of Direct-Acting Antivirals for Hepatitis C Virus in Priority Populations: A Systematic Review
Bibliographic record
Abstract
Background & Objective: Access to Hepatitis C (HCV) care remains suboptimal. This systematic review sought to identify existing interventions designed to improve direct-acting antiviral (DAA) uptake among HCV infected women, people who inject drugs (PWID), men who have sex with men (MSM), and Indigenous peoples. Methods: Studies published in high- and middle-income countries were retrieved from eight electronic databases and gray literature (e.g., articles, research reports, theses, abstracts) were screened by two independent reviewers. Identified interventions were summarized using textual narrative synthesis. Results: After screening 3,139 records, 39 studies were included (11 controlled comparative studies; 36 from high-income countries). Three groups of interventions were identified: interventions involving patients; providers; or the healthcare system. Interventions directed to patients included care co-ordination, accelerated DAA initiation, and patient education. Interventions involving providers included provider education, telemedicine, multidisciplinary teams, and general practitioner-led care. System-based interventions comprised DAA universal access policies and offering HCV services in four settings (primary care, secondary care, tertiary care, and community settings). Most studies (30/39) described complex interventions, i.e., those with two or more strategies combined. Most interventions (37/39) were tailored to, or studied among, PWID. Only one study described an intervention that was aimed at women. Conclusions: Combining multiple interventions is a common approach for supporting DAA initiation. Three main research gaps were identified, specifically, a lack of: (1) controlled trials estimating the individual or combined effects of interventions on DAA uptake; (2) studies in middle-income countries; and (3) interventions tailored to women, MSM, and Indigenous people.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".