Experiences of people who inject drugs with hepatitis C testing and their perceptions of a pharmacy-based testing option: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) are at high risk of acquiring hepatitis C virus (HCV) infection, yet many remain undiagnosed due to testing barriers. Pharmacy-based point-of-care testing could improve access; however, little is known about its acceptability among PWID. AIM: To explore the experiences of PWID with HCV testing and their perceptions of a pharmacy-based HCV testing option. METHOD: A qualitative study involving interviews with eleven PWID between June and August 2022. Participants were asked about their perceptions and experiences about HCV testing as well as their views on a proposed pharmacy-based HCV testing model which was being proposed for a separate research study. Data were analyzed using reflexive thematic analysis. RESULTS: Regarding their experiences with HCV testing, participants recognized the importance of testing to know their status both for their health and that of others. Several challenges to testing were described, and participants described the impact of the primary care provider on testing. It was suggested that opioid agonist therapy programs were a missed opportunity for testing, and many potential advantages to pharmacy testing were described. Privacy and confidentiality within the pharmacy, as well as the impact of the relationship with pharmacists and staff were key factors influencing uptake. CONCLUSION: Pharmacy-based HCV testing is viewed by participants as a convenient and acceptable testing option. Addressing stigma, ensuring privacy, and building trust with pharmacy staff appear to be critical for uptake. This approach may help to engage PWID in HCV testing as part of HCV elimination efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".