‘It Looks Like You Just Want Them When Things Get Rough’: Civil Society Perspectives on Negative Trial Results and Stakeholder Engagement in<scp>HIV</scp>Prevention Trials
Bibliographic record
Abstract
Civil society organizations (CSOs) have significantly impacted on the politics of health research and the field of bioethics. In the global HIV epidemic, CSOs have served a pivotal stakeholder role. The dire need for development of new prevention technologies has raised critical challenges for the ethical engagement of community stakeholders in HIV research. This study explored the perspectives of CSO representatives involved in HIV prevention trials (HPTs) on the impact of premature trial closures on stakeholder engagement. Fourteen respondents from South African and international CSOs representing activist and advocacy groups, community mobilisation initiatives, and human and legal rights groups were purposively sampled based on involvement in HPTs. Interviews were conducted from February-May 2010. Descriptive analysis was undertaken across interviews and key themes were developed inductively. CSO representatives largely described positive outcomes of recent microbicide and HIV vaccine trial terminations, particularly in South Africa, which they attributed to improvements in stakeholder engagement. Ongoing challenges to community engagement included the need for principled justifications for selective stakeholder engagement at strategic time-points, as well as the need for legitimate alternatives to CABs as mechanisms for engagement. Key issues for CSOs in relation to research were also raised.
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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 | 0.232 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.076 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.015 | 0.023 |
| 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".