Willingness to Use Mobile Phone Apps for HIV Prevention Among Men Who Have Sex with Men in London: Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: Many men who have sex with men (MSM) use apps to connect with and meet other MSM. Given that these apps are often used to arrange sexual encounters, it is possible that apps may be suitable venues for messages and initiatives related to HIV prevention such as those to increase HIV testing rates among this population. OBJECTIVE: The purpose of this study was to assess willingness to use a new app for reminders of when to be tested for HIV infection among a sample of MSM in London who use apps to arrange sexual encounters. METHODS: Broadcast advertisements targeted users of a popular social-networking app for MSM in London. Advertisements directed users to a Web-based survey of sexual behaviors and sexual health needs. Willingness to use apps for reminders of when to be tested for HIV was assessed. In addition, participants responded to items assessing recent sexual behaviors, substance use, and demographic characteristics. Exploratory analyses were undertaken to examine differences in willingness to use an app by demographic and behavioral characteristics. RESULTS: Broadcast advertisements yielded a sample of 169 HIV-negative MSM. Overall, two-thirds (108/169, 63.9%) reported willingness to use an app to remind them when to be tested for HIV. There were no significant differences in willingness to use these apps based on demographic characteristics, but MSM who reported recent binge drinking and recent club drug use more frequently reported willingness to use this app compared to their nonusing counterparts. CONCLUSIONS: MSM in this sample are willing to use a new app for HIV testing reminders. Given the high levels of willingness to use them, these types of apps should be developed, evaluated, and made available for this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".