Towards a Science of Community Stakeholder Engagement in Biomedical HIV Prevention Trials: An Embedded Four-Country Case Study
Bibliographic record
Abstract
Broad international guidelines and studies in the context of individual clinical trials highlight the centrality of community stakeholder engagement in conducting ethically rigorous HIV prevention trials. We explored and identified challenges and facilitators for community stakeholder engagement in biomedical HIV prevention trials in diverse global settings. Our aim was to assess and deepen the empirical foundation for priorities included in the GPP guidelines and to highlight challenges in implementation that may merit further attention in subsequent GPP iterations.From 2008-2012 we conducted an embedded, multiple case study centered in Thailand, India, South Africa and Canada. We conducted in-depth interviews and focus groups with respondents from different trial-related subsystems: civil society organization representatives, community advocates, service providers, clinical trialists/researchers, former trial participants, and key HIV risk populations. Interviews/focus groups were recorded, and coded using thematic content analysis. After intra-case analyses, we conducted cross-case analysis to contrast and synthesize themes and sub-themes across cases. Lastly, we applied the case study findings to explore and assess UNAIDS/AVAC GPP guidelines and the GPP Blueprint for Stakeholder Engagement.Across settings, we identified three cross-cutting themes as essential to community stakeholder engagement: trial literacy, including lexicon challenges and misconceptions that imperil sound communication; mistrust due to historical exploitation; and participatory processes: engaging early; considering the breadth of "community"; and, developing appropriate stakeholder roles. Site-specific challenges arose in resource-limited settings and settings where trials were halted.This multiple case study revealed common themes underlying community stakeholder engagement across four country settings that largely mirror GPP goals and the GPP Blueprint, as well as highlighting challenges in the implementation of important guidelines. GPP guidance documents could be strengthened through greater focus on: identifying and addressing the community-specific roots of mistrust and its impact on trial literacy activities; achieving and evaluating representativeness in community stakeholder groups; and addressing the impact of power and funding streams on meaningful engagement and independent decision-making.
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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 | 0.125 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.010 | 0.011 |
| 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".