Using eHealth to Reach Black and Hispanic Men Who Have Sex With Men Regarding Treatment as Prevention and Preexposure Prophylaxis: Protocol for a Small Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Black and Hispanic men who have sex with men in the United States continue to be disproportionately affected by HIV and AIDS. Uptake of and knowledge about biobehavioral HIV prevention approaches, such as treatment as prevention and preexposure prophylaxis, are especially low in these populations. eHealth campaigns and social media messaging about treatment as prevention and preexposure prophylaxis may help to fill this gap in knowledge and lead to increased uptake of such strategies; however, no evidence exists of the effects of these targeted forms of communication on treatment as prevention and preexposure prophylaxis uptake in these populations. OBJECTIVE: We describe the protocol for a 3-part study aiming to develop and evaluate an eHealth intervention with information about treatment as prevention and preexposure prophylaxis for HIV-positive and HIV-negative black and Hispanic men who have sex with men. METHODS: Phases 1 and 2 will involve focus groups and cognitive interviews with members of the target populations, which we will use to create a culturally tailored, interactive website and applicable social media messaging for these men. Phase 3 will be a small randomized controlled trial of the eHealth intervention, in which participants will receive guided social media messages plus the newly developed website (active arm) or the website alone (control arm), with assessments at baseline and 6 months. RESULTS: Participant recruitment began in August 2017 and will end in August 2020. CONCLUSIONS: Public health interventions are greatly needed to increase knowledge about and uptake of biobehavioral HIV prevention strategies such as treatment as prevention and preexposure prophylaxis among black and Hispanic men who have sex with men. eHealth communication campaigns offer a strategy for engaging these populations in health communication about biobehavioral HIV prevention. TRIAL REGISTRATION: ClinicalTrials.gov NCT03404531; https://www.clinicaltrials.gov/ct2/show/NCT03404531 (Archived by WebCite at http://www.webcitation.org/70myofp0R). REGISTERED REPORT IDENTIFIER: RR1-10.2196/11047.
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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 | 0.047 | 0.046 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.087 | 0.017 |
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".