Randomized Controlled Trials of Technology-Based HIV/STI and Drug Abuse Preventive Interventions for African American and Hispanic Youth: Systematic Review
Bibliographic record
Abstract
BACKGROUND: HIV/sexually transmitted infections (STIs) and drug abuse remain significant public health concerns in the United States, and African American and Hispanic youth are disproportionately affected. Although technology-based interventions are efficacious in preventing and reducing HIV/STI and licit/illicit drug use behaviors, relatively little is known regarding the state of the science of these interventions among African American and Hispanic youth. OBJECTIVE: The aim of this review is to identify and examine randomized controlled trials (RCTs) of technology-based HIV/STI and/or drug abuse preventive interventions for African American and Hispanic youth. METHODS: We searched electronic databases (ie, PubMed, Proquest, PsycINFO, Ebscohost, Google Scholar) to identify studies between January 2006 and October 2016. RCTs of technology-based interventions targeting African American and Hispanic youth HIV/STI risk behaviors, including sexual risk, licit and illicit drug use, and HIV/STI testing were included. RESULTS: Our search revealed a total of three studies that used an RCT design and included samples comprised of >50% African American and/or Hispanic youth. The follow-up assessments ranged from two weeks to six months and the number of participants in each trial ranged from 72 to 141. The three interventions were theory-driven, interactive, and tailored. The long-term effects of the interventions were mixed, and outcomes included reductions in sex partners, licit drug use, and condomless anal sex acts. CONCLUSIONS: Although technology-based interventions seem promising in the prevention of HIV/STI and drug abuse among African American and Hispanic youth, more research is needed.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".