A Community-Engaged Approach to Creating a Mobile HIV Prevention App for Black Women: Focus Group Study to Determine Preferences via Prototype Demos
Bibliographic record
Abstract
BACKGROUND: Black women are an important but relatively overlooked at-risk group in HIV prevention efforts. Although there is an aggregate decline of HIV diagnoses among women in the United States, there are persistent disparate rates of new HIV infections among Black women compared to any other cisgender female subgroup. Strategies to end the HIV epidemic-as outlined in the Ending the HIV Epidemic initiative-for all communities must consider HIV prevention messaging and message delivery mediums that are created with community input. Although mobile health (mHealth) is a popular platform for delivering HIV interventions, there are currently no mobile apps that consider cisgender Black women with the goal of promoting a comprehensive women's reproductive health and HIV prevention lifestyle. Previous research recommends inclusion of the target population from project inception and iteratively throughout development, to promote use of the intervention. OBJECTIVE: The purpose of this study is to understand cisgender Black women's preferences for functionality, format, and design of a mobile HIV prevention app and to examine their willingness to use an app for HIV prevention. METHODS: We conducted a series of four focus groups with 23 Black cisgender women. Focus groups included discussion and demonstration elements to address cisgender women's general preference for apps, HIV prevention content that would be useful in an app, and preferred app features that would promote use of an HIV-centric app. During focus group discussions, participants were shown narrated, custom wireframes of HIV prevention app prototypes to demonstrate potential app function. RESULTS: Findings indicated the presence of eight subthemes within the coding structure of three overall themes: (1) health content within the mobile app, (2) mobile app functionality, format, and design, and (3) other suggested features. Specifically, participants detailed preferred educational content, content distribution, app aesthetics, privacy considerations, and marketing of the app. CONCLUSIONS: Findings suggest that Black cisgender women preferred an app that integrated HIV prevention and optimal sexual health promotion. Participants provided a range of preferences for content integration and facilitators of app engagement with an HIV prevention app. Preferences centered on gender and cultural congruency of information and content, evidenced by visuals, language, and resources. Black cisgender women are viable consumers for a mobile app-based HIV prevention intervention.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".