Digital Health App to Address Disparate HIV Outcomes Among Black Women Living in Metro-Atlanta: Protocol for a Multiphase, Mixed Methods Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Cisgender Black women in the southern United States are at heightened risk for HIV and adverse sexual and reproductive health outcomes. Mobile health interventions that target HIV risk while being adapted to the needs and lived experiences of Black women are remarkably limited. OBJECTIVE: The study aims to refine SavvyHER, a mobile app for HIV prevention, with Black women residing in high HIV incidence areas of Georgia and evaluate the feasibility, acceptability, and usability of SavvyHER. This paper describes the procedures implemented to conduct this research. METHODS: Community-based participatory research tenets guide this multiphase study to finalize the development of what we hypothesize will be an effective, sustainable, and culturally relevant HIV prevention and optimal sexual health and reproductive wellness app for Black women. This multiphased, mixed methods study consists of 3 phases. The first phase entails focus groups with Black women to understand their preferences for the functionality and design of a beta prototype version of SavvyHER. In the second phase, an app usability pretest (N=10) will be used to refine and optimize the SavvyHER app. The final phase will entail a pilot randomized controlled trial (N=60) to evaluate the app's feasibility and usability in preparation for a larger trial. RESULTS: Findings from preliminary focus groups revealed educational content, app aesthetics, privacy considerations, and marketing preferred by Black women, thus informing the first functional SavvyHER prototype. As we adapt and test the feasibility of SavvyHER, we hypothesize that the app will be an effective, sustainable, and culturally relevant HIV prevention, sexual health, and reproductive wellness tool for Black women. CONCLUSIONS: The findings from this research substantiate the importance of developing health interventions curated for and by Black women to address critical HIV disparities. The knowledge gained from this research can reduce HIV disparities among Black women through a targeted intervention that centers on their health needs and priorities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42712.
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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 | 0.034 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".