Barriers to accessing hepatitis B medication: a qualitative study from the USA and Canada
Bibliographic record
Abstract
OBJECTIVES: To collect and document the numerous barriers that people living with hepatitis B (PLHB) encounter when trying to access their hepatitis B virus (HBV) medications. DESIGN: Researchers collected qualitative data through 24 online interviews. The semistructured interview questions focused on the impact that HBV has on different aspects of daily life (physical, emotional and social), personal experiences managing their infection, HBV treatment experiences and interactions with healthcare providers. SETTING: All interviews occurred over Zoom. PARTICIPANTS: The participant cohort consisted of 12 males and 12 females. 63% of all participants represented communities of colour (37% white, 17% black/African/African American and 46% Asian/Asian American). Most of the participants were on antiviral treatment at the time of the study (62%). Participants were PLHB (self-reported), ≥18 years old, living in the USA or Canada and spoke English. RESULTS: Participants reported several barriers to accessing medicine among PLHB including financial barriers, health insurance and pharmacy preauthorisation process and other intangible barriers like lack of access to reliable patient-friendly information and stigma. The identified barriers to accessing HBV medication impacted patients' continuity of care. CONCLUSIONS: Access to medicine is essential to improving health outcomes. PLHB experience significant barriers to accessing HBV antivirals at different levels. Patient-related, physician-related and healthcare system barriers were identified as themes contributing to antiviral access challenges. More research is needed to identify strategies to improve access to HBV medications.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".