Implementing a Digital HIV Care Navigation Intervention (Health eNav): Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Young racial and ethnic minority men who have sex with men (MSM) and trans women are disproportionately affected by HIV and AIDS in the United States. Unrecognized infection, due to a low uptake of HIV testing, and poor linkage to care are driving forces of ongoing HIV transmission among young racial and ethnic minority MSM and trans women. Internet and mobile technologies, in combination with social network-based approaches, offer great potential to overcome and address barriers to care and effectively disseminate interventions. OBJECTIVE: We describe Health eNavigation (Health eNav), a digital HIV care navigation intervention that extends supportive care structures beyond clinic walls to serve youth and young adults living with HIV who are newly diagnosed, not linked to care, out of care, and not virally suppressed, at times when they need support the most. METHODS: This study leverages ecological momentary assessments for a period of 90 days and uses person-delivered short message service text messages to provide participants with digital HIV care navigation over a 6-month period. We aim to improve engagement, linkage, and retention in HIV care and improve viral suppression. Digital HIV care navigation includes the following components: (1) HIV care navigation, (2) health promotion, (3) motivational interviewing, and (4) digital social support. RESULTS: Recruitment began on November 18, 2016; enrollment closed on May 31, 2018. Intervention delivery ended on November 30, 2018, and follow-up evaluations concluded on October 31, 2019. In this paper, we present baseline sample characteristics. CONCLUSIONS: We discuss real-world strategies and challenges in delivering the digital HIV care navigation intervention in a city-level, public health setting. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16406.
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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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.020 |
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".