Refining a Multicomponent Intervention to Increase Perceived HIV Risk and PrEP Initiation: Focus Group Study Among Black Sexual Minority Men
Bibliographic record
Abstract
BACKGROUND: Increased preexposure prophylaxis (PrEP) initiation is needed to substantially decrease HIV incidence among Black sexual minority men (BSMM). However, BSMM perceive others as PrEP candidates instead of themselves and are less likely than other groups to use PrEP if prescribed. Peers and smartphone apps are popular HIV prevention intervention tools typically used independently. However, they could be useful together in a multicomponent strategy to improve perceived HIV risk and PrEP initiation for this group. Information regarding attitudes and preferences toward this multicomponent strategy is limited. OBJECTIVE: The goal of this study is to obtain attitudes and perspectives regarding the design of a multicomponent intervention that uses a smartphone app and a peer change agent (PCA) to increase perceived HIV risk and PrEP initiation. The intervention will be refined based on thematic findings for a culturally responsive approach. METHODS: Data were obtained guided by life course theory and the health belief model using 12 focus groups and 1 in-depth interview among HIV-negative BSMM from Baltimore, MD, between October 2019 and May 2020 (n=39). Groups were stratified by the following ages: 18 to 24 years, 25 to 34 years, and 35 years and older. Participants were provided details regarding an existing mobile app diary to self-monitor sexual behaviors and a hypothetical PCA with whom to review the app. Facilitators posed questions regarding perceived HIV risk, attitudes toward the app, working with a PCA, and preferences for PCA characteristics and approaches. RESULTS: Most participants identified as homosexual, gay, or same gender-loving (26/38, 68%), were employed (26/38, 69%), single (25/38, 66%), and interested in self-monitoring sexual behaviors (28/38, 68%). However, themes suggested that participants had low perceived HIV risk, that self-monitoring sexual behaviors using a mobile app diary was feasible but could trigger internalized stigma, and that an acceptable PCA should be a possible self for BSMM to aspire to but they still wanted clinicians to "do their job." CONCLUSIONS: HIV-negative BSMM have dissonant attitudes regarding perceived HIV risk and the utility of a mobile app and PCA to increase perceived HIV risk and PrEP initiation. Future research will explore the feasibility, acceptability, and preliminary impact of implementing the multicomponent intervention on perceived HIV risk and PrEP initiation among BSMM in a pilot study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".