eHealth-Enhanced Peer Navigation for Substance Use Treatment and HIV Prevention Service Linkage for Young Adults Surveilled by the Criminal Legal System: Protocol for a Pilot Randomized Trial Study
Bibliographic record
Abstract
BACKGROUND: In the United States, the proportion of criminal legal-involved (CLI) adults with a substance use disorder reaches 72%, and ~150,000 persons with HIV pass through a carceral setting annually, which represents 16% of the HIV-infected population nationally. Despite the high need for substance use treatment and HIV prevention services, few carceral settings successfully link CLI individuals to treatment upon release. Young adults represent 41.9% of the adults incarcerated in the United States and have the highest HIV incidence rates nationally. Peer patient navigation has successfully increased community-based care linkage for people living with HIV leaving jail; yet, peer-led navigation for HIV prevention among HIV-negative CLI populations is undeveloped and untested. eHealth approaches to substance use and HIV prevention services hold promise because they improve access to effective intervention services, particularly for younger people. OBJECTIVE: This paper describes a protocol for a pilot randomized controlled trial that aims to improve linkage to substance use treatment and HIV prevention services using peer navigation and a codeveloped eHealth technology adjunct. METHODS: The three aims of this study are to (1) adapt an existing evidence-based navigator model and incorporate codeveloped eHealth technology to refer and link young adults (18 to 29 years) surveilled by the criminal legal system to substance use and pre-exposure prophylaxis (PrEP) services; (2) refine and test the intervention with criminal legal-involved young adults (CLI-YAs); and (3) assess the feasibility, acceptability, and impact of the intervention. Data to inform the intervention will be collected via system partner interviews (n=4) and focus groups with CLI-YAs (n=24). Next, an open trial (n=10) will be conducted. The intervention will be refined via interviews with participants and facilitators, and a randomized pilot trial (n=75) will be conducted to assess the feasibility, acceptability, and preliminary impact of the eHealth-enhanced navigation on substance use and PrEP services linkage. Exit interviews conducted with a subsample of intervention participants (n=10), the navigator (n=1), and system partners (n=4) will assess intervention acceptability and suggestions for improvement. A community of practice, a group of system partners with an interest in working toward solutions to common problems, will inform each phase of the study. RESULTS: The project is currently ongoing. The project was funded in September 2022. Internal review board approval was received on March 21, 2022. The first results from early study aims are expected to be published in 2025. CONCLUSIONS: This study provides an opportunity to reduce HIV acquisition and improve access to substance use treatment in a systemically marginalized group: young CLI-YAs. The results will contribute to the development and testing of a future multilevel randomized controlled trial. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54815.
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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 | 0.040 | 0.038 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.012 |
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".