A Mobile Sexual Health App on Empowerment, Education, and Prevention for Young Adult Men (MyPEEPS Mobile): Acceptability and Usability Evaluation
Bibliographic record
Abstract
BACKGROUND: HIV incidence among young adult men who have sex with men (MSM), particularly among black and Latino men, continues to rise. As such, continued HIV prevention interventions for young MSM of color are of utmost importance. Male Youth Pursuing Empowerment, Education and Prevention around Sexuality (MyPEEPS) Mobile is a comprehensive HIV prevention and sexual health education smartphone app initially created to promote sexual health and HIV prevention among adolescent sexual minority young men aged 13 to 18 years. OBJECTIVE: The objective of this study was to critically appraise the acceptability and usability of MyPEEPS Mobile for young adult MSM aged 19 to 25 years. METHODS: Study participants used the mobile app, completed usability questionnaires and in-depth interviews, and reported their experience using the app. Analysis of interview data was guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) to better understand the usability and acceptability of this intervention for young adults. Interview data were coded using the following constructs from the UTAUT model: performance expectancy, effort expectancy, and social influence. RESULTS: A total of 20 young adult MSM (n=10 in Chicago, Illinois, and n=10 in New York, New York) were enrolled in the study. Participants reported that MyPEEPS Mobile was free of functional problems (Health Information Technology Usability Evaluation Scale scores and Post-Study System Usability Questionnaire scores consistent with high usability), easy to use, and useful, with an engaging approach that increased acceptability, including the use of avatars and animation, and inclusive representation of the diverse identities by race and ethnicity, gender identity, and sexual orientation. Recommended areas for improving MyPEEPS Mobile for the target demographic included more adult-oriented graphics, advanced educational content, scenarios for youth with more sexual experience, and search function to increase accessibility of key content. CONCLUSIONS: Overall, young adult MSM aged 19 to 25 years described the MyPEEPS Mobile as educational, informative, and usable for their sexual health education and HIV prevention needs, and they provided actionable recommendations to optimize its use and applicability for this age group.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".