Mobile Technology Use and Acceptability of mHealth for HIV Prevention Among Men Who Have Sex With Men in Malaysia
Bibliographic record
Abstract
Background The growth in mobile technology access, utilization, and services holds great promise for facilitating HIV prevention efforts in Malaysia. Despite these promising trends, there is a dearth of evidence on the use of mobile health (mHealth) platforms to address the HIV prevention needs of Malaysian men who have sex with men (MSM). Objective The goal of this study was to gain insights into (1) the access and utilization of communication technology (eg, landline phone, internet, and cell phone), (2) the acceptability of mHealth-based interventions for HIV prevention services, and (3) preferences regarding the format and frequency of mHealth interventions among Malaysian MSM. Methods A cross-sectional survey of 376 Malaysian MSM was conducted between July 2018 and March 2020. Participants were recruited using respondent-driven sampling in the Greater Kuala Lumpur region, Malaysia. Participants completed a self-administered assessment of participant demographics, HIV risk-related behaviors, access to and frequency of the use of communication technology, and the acceptability of mHealth for HIV prevention. Results Almost all participants owned or had access to a smartphone with internet access (97.9%) and accessed the internet daily (99.2%), mainly on a smartphone (88.8%). Using a 5-point scale, participants on average used smartphones primarily for social networking (mean 4.5, SD 0.8), followed by sending or receiving emails (mean 4.0, SD 1.0) and searching for health-related information (mean 3.5, SD 0.9). Further, the results indicated the high acceptance of mHealth for HIV prevention, that is, receiving HIV prevention information (91.8%), receiving reminders to take medications (89.4%), tracking sexual activity (81.4%), tracking drug use (74.7%), and monitoring drug cravings (74.5%), with the most preferred method being the smartphone app for all activities. Conclusions The findings from this study provide support for developing and deploying mHealth strategies for HIV prevention in MSM by using a smartphone app, which are crucial for a key population with suboptimal engagement in HIV prevention and treatment. Conflicts of Interest None declared.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".