‘<i>I just never wanted them to feel uncomfortable</i>’: Barriers to pharmacy-based identification and treatment of hepatitis C in Victoria, Canada
Bibliographic record
Abstract
Background: Canada is currently on target to reach the 2030 WHO goal of HCV elimination. Continued high rates of treatment are required to meet this goal. Novel models such as Tayside, Scotland pharmacy-based HCV screening and treatment have proven successful to engage people who use drugs (PWUD) in HCV therapy with a simplified, task-shifted cascade of care. This study seeks to determine whether these successes can be replicated at community pharmacies in Victoria BC. Methods: Four pharmacies who work with PWUD and provide opioid agonist therapy were trained to provide consent and perform point-of-care HCV antibody screening. They were supported by study nurse to link to HCV RNA testing when antibody positive patients were identified, with HCV treatment offered to RNA positive participants. Qualitative interviews were conducted with five pharmacy staff to explore experiences and feasibility of pharmacists in HCV care cascade. Results: Pharmacy staff completed 200 HCV OraQuick tests between October 2020 and June 2022: 65 HCV antibody positive, 29 HCV RNA negative (25 previously treated and 4 self-cleared). Of the 26 RNA positive participants, one is awaiting treatment, 25 people have started treatment, 22 achieving SVR. Although the onset of the COVID-19 pandemic was a fundamental barrier incorporating HCV testing at pharmacies, stigma related to HCV and illicit drug use continues to impact this process. Conclusions: This innovative pharmacy-based approach found people with limited connection to primary health care to test and treat HCV but requires more training and support to be more widely feasible.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".