Research Priorities to End the Adolescent HIV Epidemic in the United States: Viewpoint
Bibliographic record
Abstract
Youth represent 21% of new HIV diagnoses in the United States. Gay, bisexual, and transgender (GBT) youth, particularly those from communities of color, and youth who are homeless, incarcerated, in institutional settings, or engaging in transactional sex are most greatly impacted. Compared with adults, youth have lower levels of HIV serostatus awareness, uptake of antiretroviral therapy (ART), and adherence. Widespread availability of ART has revolutionized prevention and treatment for both youth at high risk for HIV acquisition and youth living with HIV, increasing the need to integrate behavioral interventions with biomedical strategies. The investigators of the Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) completed a research prioritization process in 2019, focusing on research gaps to be addressed to effectively control HIV spread among American youth. The investigators prioritized research in the following areas: (1) innovative interventions for youth to increase screening, uptake, engagement, and retention in HIV prevention (eg, pre-exposure prophylaxis) and treatment services; (2) structural changes in health systems to facilitate routine delivery of HIV services; (3) biomedical strategies to increase ART impact, prevent HIV transmission, and cure HIV; (4) mobile technologies to reduce implementation costs and increase acceptability of HIV interventions; and (5) data-informed policies to reduce HIV-related disparities and increase support and services for GBT youth and youth living with HIV. ATN's research priorities provide a roadmap for addressing the HIV epidemic among youth. To reach this goal, researchers, policy makers, and health care providers must work together to develop, test, and disseminate novel biobehavioral interventions for youth.
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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 | 0.037 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".