Game Plan: Development of a Web App Designed to Help Men Who Have Sex With Men Reduce Their HIV Risk and Alcohol Use
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) are at high risk for HIV, and alcohol use is a major risk factor for HIV infection. Internet-facilitated brief interventions have been shown to reduce alcohol use and HIV-risk behavior in other at-risk populations, but have so far incorporated limited content and have not been tested among MSM. OBJECTIVE: This manuscript describes Game Plan, an interactive, tablet-optimized web application designed to help heavy drinking, high-risk MSM consider reducing their alcohol use and sexual risk behavior. In this paper, we discuss the rationale, goals, and flow for each of Game Plan's components, which were modelled after common in-person and web-based brief motivational interventions for these behaviors. METHODS: The development of Game Plan was informed by a thorough user-focused design research process that included (1) audits of existing interventions, (2) focus groups with stakeholders and (3) intended users (high-risk, heavy drinking MSM), and (4) usability testing. The aesthetic, features, and content of the app were designed iteratively throughout this process. RESULTS: The fully-functional Game Plan app provides (1) specific and personal feedback to users about their level of risk, (2) exercises to help prompt users to reflect on whether their current behavior aligns with other important life goals and values, and for those open to change, (3) exercises to help users understand factors that contribute to risk, and (4) a change planning module. In general, this flow was constructed to roughly align with the two phases described in early accounts of motivational interviewing (MI): (1) Content intended to elicit intrinsic motivation for change, and when/if sufficient motivation is present, (2) content intended to translate that motivation into specific goals and plans for change. This sequence first focuses on the user's HIV risk behavior, followed by their alcohol use and the connection between the two. The app's overall aesthetic (eg, branding, color palettes, icons/graphics) and its onboarding sequence was also designed to align with the "spirit" of MI by conveying respect for autonomy, open-mindedness (ie, avoiding judgment), and empathy. CONCLUSIONS: Should future research support its efficacy in facilitating behavior change, Game Plan could represent a wide-reaching and scalable tool that is well-suited for use in settings where delivering evidence-based, in-person interventions would be difficult or cost-prohibitive.
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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 | 0.003 |
| 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) | 0.007 | 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".