“I want to get better, but…”: identifying the perceptions and experiences of people who inject drugs with respect to evolving hepatitis C virus treatments
Bibliographic record
Abstract
BACKGROUND: The advent of highly tolerable and efficacious direct-acting antiviral (DAA) medications has transformed the hepatitis C virus (HCV) treatment landscape. Yet, people who inject drugs (PWID) - a population with inequitably high rates of HCV and who face significant socio-structural barriers to healthcare access - continue to have disproportionately low rates of DAA uptake. The objective of this study is to explore how PWID with lived experience of HCV perceive and experience DAA treatment, in a setting with universal coverage of these medications since 2018. METHODS: Informed by a critical interpretive framework, we thematically analyze data from in-depth, semi-structured interviews conducted between January and June 2018 in Vancouver, Canada, with a purposive sample (n = 56) of PWID at various stages (e.g., pre, peri, post) of DAA treatment. RESULTS: The analysis yielded three key themes: (i) life with HCV, (ii) experiences with and perceptions of evolving HCV treatments, and (iii) substance use and the uptake of DAA treatments. First, participants described how health and healthcare conditions, such as the deprioritizing of HCV (e.g., due to: being asymptomatic, healthcare provider inaction, gatekeeping) and catalysts to care (e.g., symptom onset, treatment for co-morbidities) shaped DAA treatment motivation and access. Second, participants described how individual and community-level accounts of evolving HCV treatments, including skepticism following negative experiences with Interferon-based treatment and uncertainty regarding treatment eligibility, negatively influenced willingness and opportunities to access DAAs. Concurrently, participants described how peer and community endorsement of DAAs was positively associated with treatment uptake. Third, participants favoured HCV care that was grounded in harm reduction, which included the integration of DAAs with other substance use-related services (e.g., opioid agonist therapy, HIV care), and which was often contrasted against abstinence-focused care wherein substance use is framed as a contraindication to HCV treatment access. CONCLUSIONS: These findings underscore several equity-oriented healthcare service delivery and clinician adaptations that are required to scale up DAAs among PWID living with HCV, including the provision of harm reduction-focused, non-stigmatizing, integrated, and peer-led care that responds to power differentials.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".