Boosting Diverse Communities Participation in Clinical Trials: What People Living with Hepatitis B Must Know
Bibliographic record
Abstract
Introduction: Globally, people from African, Asian and Western Pacific regions are disproportionately affected by hepatitis B virus (HBV) and are underrepresented in HBV clinical trials (trials). This study explored trial knowledge and misconceptions, sources of information, and community recommendations to enhance hepatitis B trial participation. Methods: This mixed-methods study combined an online global survey followed by semi-structured interviews and focus groups (FGs) across 10 diverse communities in the U.S. Qualitative data collection was informed by initial survey insights. Participants were≥18 years old and living with HBV. Results: In total, there were 680 survey respondents and 36 qualitative participants (30 FG participants, 6 interviewees). Survey respondents reported some of the classical challenges to trial participation including limited awareness of trials (73%) and concerns about trial safety (41%). Qualitative participants elaborated on these challenges, discussing difficulties accessing reliable information and misinformation about their eligibility. Despite these challenges, survey (85%) and qualitative participants expressed a high willingness to participate in HBV trials if they were properly informed. Participants identified community-specific strategies to enhance trial awareness, provide accurate information, and address common misperceptions. While healthcare providers and local community organizations were recognized as trusted sources of information, they were among the least frequently reported sources for learning about trials. Conclusion: This study shows that with accurate and accessible information, people living with HBV are more likely to consider participating in clinical trials. However, they are often not exposed to informative trial communications. This limited access fosters misperceptions and fear, hindering hepatitis B trial participation. Community-informed outreach strategies, such as engaging healthcare providers and community-based organizations to deliver culturally tailored and linguistically appropriate education, can enhance community engagement in trials, ensuring diverse and representative participation in HBV trials.
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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 | 0.030 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".