An mHealth Intervention to Improve Young Gay and Bisexual Men’s Sexual, Behavioral, and Mental Health in a Structurally Stigmatizing National Context
Bibliographic record
Abstract
BACKGROUND: Young gay and bisexual men (YGBM) in some Eastern European countries, such as Romania, face high stigma and discrimination, including in health care. Increasing HIV transmission is a concern given inadequate prevention, travel to high-prevalence countries, and popularity of sexual networking technologies. OBJECTIVE: This study aimed to adapt and pilot test, in Romania, a preliminarily efficacious mobile health (mHealth) HIV-prevention intervention, created in the United States, to reduce HIV risk among YGBM. METHODS: After an intervention formative phase, we enrolled 43 YGBM, mean age 23.2 (SD 3.6) years, who reported condomless sex with a male partner and at least 5 days of heavy drinking in the past 3 months. These YGBM completed up to eight 60-minute text-based counseling sessions grounded in motivational interviewing and cognitive behavioral skills training with trained counselors on a private study mobile platform. We conducted one-group pre-post intervention assessments of sexual (eg, HIV-risk behavior), behavioral (eg, alcohol use), and mental health (eg, depression) outcomes to evaluate the intervention impact. RESULTS: From baseline to follow-up, participants reported significant (1) increases in HIV-related knowledge (mean 4.6 vs mean 4.8; P=.001) and recent HIV testing (mean 2.8 vs mean 3.3; P=.05); (2) reductions in the number of days of heavy alcohol consumption (mean 12.8 vs mean 6.9; P=.005), and (3) increases in the self-efficacy of condom use (mean 3.3 vs mean 4.0; P=.01). Participants reported significant reductions in anxiety (mean 1.4 vs mean 1.0; P=.02) and depression (mean 1.5 vs mean 1.0; P=.003). The intervention yielded high acceptability and feasibility: 86% (38/44) of participants who began the intervention completed the minimum dose of 5 sessions, with an average of 7.1 sessions completed; evaluation interviews indicated that participation was rewarding and an "eye-opener" about HIV risk reduction, healthy identity development, and partner communication. CONCLUSIONS: This first mHealth HIV risk-reduction pilot intervention for YGBM in Eastern Europe indicates preliminary efficacy and strong acceptability and feasibility. This mobile prevention tool lends itself to broad dissemination across various similar settings pending future efficacy testing in a large trial, especially in contexts where stigma keeps YGBM out of reach of affirmative health interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".