Challenges for HIV Vaccine Dissemination and Clinical Trial Recruitment: If We Build It, Will They Come?
Bibliographic record
Abstract
HIV vaccine availability does not guarantee uptake. Given suboptimal uptake of highly efficacious and already accessible vaccines in the United States, low vaccine coverage in the developing world, and the expectation that initial HIV vaccines will be only partially efficacious, the public health community will face formidable challenges in disseminating U.S. Food and Drug Administration (FDA)-approved HIV vaccines. HIV/AIDS stigma, fear of vaccine- induced HIV infection, social side effects of testing HIV-positive, and mistrust of government and research present additional obstacles to HIV vaccine dissemination. Increased risk behaviors because of HIV vaccine availability can undermine the effectiveness of partially efficacious vaccines in reducing HIV incidence. HIV vaccine efficacy trials also face significant challenges in recruitment of sufficient volunteers and possible increases in risk behaviors due to trial participation. Planning and designing interventions to facilitate successful recruitment for large-scale phase 3 efficacy trials is a vital step towards U.S. FDA-approved HIV vaccines. Rather than despair in the face of momentous HIV vaccine dissemination challenges, or presume unrealistically that vaccine uptake will ensue automatically and that risk behavior increases will not occur, let us deem the estimated 10-year window to an approved HIV vaccine as an opportunity to investigate and confront these challenges. A consumer research agenda founded on social marketing principles is needed to facilitate the design of empirically-based interventions tailored to the unique needs and preferences of specific segments of consumers. Social marketing interventions may increase future HIV vaccine uptake and clinical trial participation, and mitigate increases in HIV risk behaviors.
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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 | 0.547 | 0.712 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.032 | 0.057 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.051 | 0.053 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".