Game Plan, a Web-Based Intervention to Improve Adherence and Persistence to HIV Pre-exposure Prophylaxis and Reduce Heavy Drinking in Gay, Bisexual, and Other Men Who Have Sex With Men: Usability and User Experience Testing
Bibliographic record
Abstract
BACKGROUND: Encouraging consistent use of pre-exposure prophylaxis (PrEP) is essential for reducing HIV incidence, particularly among gay, bisexual, and other men who have sex with men (GBM), and especially those who engage in heavy drinking. Although practice guidelines recommend providing adherence counseling to PrEP patients, clinics and providers may not have the resources or expertise to provide it. Internet-facilitated interventions have been shown to improve HIV prevention outcomes, including medication and care adherence. Game Plan is a website we created to help users make a tangible plan for reducing their HIV risk. We designed additional components of Game Plan to address key individual level barriers to PrEP use. OBJECTIVE: The aim of this mixed methods research is to test the usability and user experience of these components with intended users: GBM who drink heavily and are on PrEP. METHODS: In study 1 (usability), we completed a detailed individual interview in which participants (n=10) walked through a prototype of the website, thinking aloud as they did, and completed a follow-up interview and web-based survey afterward. Study 2 (user experience) involved providing participants (n=40) with a link to the prototype website to explore on their own and asking them to complete the same follow-up survey afterward. Qualitative data were analyzed using thematic analysis, and descriptive statistics were used to analyze quantitative data. RESULTS: Users in both studies gave the website excellent ratings for usability, overall satisfaction, and quality, and most often described the site as informative, helpful, and supportive. Users also rated the site's content and feel as respectful of them and their autonomy, empathetic, and they stated that it conveyed confidence in their ability to change. The study 1 interviews highlighted the importance of the website's esthetics to the participants' engagement with it and its credibility in prompting genuine reflection. CONCLUSIONS: GBM who reported heavy drinking and used PrEP generally found a website focused on helping them to create a plan to use PrEP consistently to be helpful. Adopting user-centered design methods and attending to the esthetics of mobile health interventions are important steps toward encouraging engagement and reducing at-risk behaviors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".