Barriers and facilitators related to HCV treatment uptake among HIV coinfected populations in Canada: Patient and treatment provider perceptions
Bibliographic record
Abstract
BACKGROUND: Direct-acting antiviral (DAA) uptake is challenging across HIV-hepatitis C (HCV) coinfected populations. This study sought to identify barriers and facilitators related to DAA uptake in priority populations in Canada. METHODS: This qualitative descriptive study included 11 people living with HIV with a history of HCV and 15 HCV care providers. Participants were part of either nominal groups (n = 4) or individual interviews (n = 6) in which they identified and ranked barriers and facilitators to DAA uptake. Consolidated lists of barriers and facilitators were identified thematically. RESULTS: Patient participants highly ranked the following barriers: competing priorities and needs (ie, social instability and mental health), delays in care, lack of adherence, and polypharmacy. Provider participant top barriers were the following: competing priorities and needs (ie, social chaos), delays in care (eg, systemic barriers, difficulties engaging patients, lack of trained HCV providers), and HCV-related stigma. Patient participants identified having a strong network of health care providers, family, and friends, possessing intrinsic motivation, and DAAs being a simple and tolerable oral treatment as important facilitators. Provider participant top-ranked facilitators were having resources to identify hard-to-reach populations (eg, patient navigation, outreach), holistic care and addiction management, provider HCV education, and a strong network of interprofessional collaboration. CONCLUSION: The barriers to DAA initiation addressed by patients and providers overlapped, with some nuances. Multidisciplinary care fostering a strong supportive network and intrinsically motivated patients along with HCV education emerged as key facilitators. This study provides insights for developing potential strategies to improve DAA uptake among HIV-HCV coinfected people in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".