Mobile Technology for Healthy Aging Among Older HIV-Positive Black Men Who Have Sex with Men: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: People living with HIV are living longer in the United States as a result of antiretroviral therapy. Black men who have sex with men (MSM) are disproportionally affected by HIV and have low rates of engagement in HIV care and treatment. Mobile technology holds promise as an intervention platform; however, little is known regarding its use among older black MSM living with HIV. OBJECTIVE: The goal of this study was to explore mobile technology use and narratives of aging with HIV among older black MSM to inform mobile health intervention development. METHODS: A total of 12 black MSM living with HIV, aged 50 years or older, completed in-person, semistructured interviews exploring the issues of aging, HIV care engagement, and mobile technology use. The interviews were audiotaped, transcribed, and analyzed using qualitative research methods. RESULTS: Men appreciated having survived the AIDS epidemic, but some expressed discomfort and ambivalence toward aging. Men described various levels of engagement in HIV care and treatment; challenges included social isolation and need for support that was not focused on HIV. Almost all described using mobile technology to engage in health care, whereas some referenced important barriers and challenges to technology use. CONCLUSIONS: Findings highlighted a high level of interest toward a mobile technology-based intervention targeting older black men but also identified barriers and challenges to using mobile technology for health care engagement. Mobile technology is well incorporated into older black MSM's lives and shows potential as an intervention platform for addressing aging issues to enhance engagement in HIV care and treatment.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".