Mobile Apps for HIV and Sexually Transmitted Infection Prevention in Canada, Mexico, and the United States: Environmental Scan
Bibliographic record
Abstract
BACKGROUND: Canada, Mexico, and the United States are primary transit destinations for migrants in the Western Hemisphere. Migrants face barriers to accessing health services, including HIV/AIDS and STI prevention. Mobile apps may enhance public health access for these populations. OBJECTIVE: This study systematically identifies and evaluates mobile apps supporting HIV and STI prevention in Canada, Mexico, and the United States. METHODS: An environmental scan of 357 mobile applications from the Google Play and Apple App stores was conducted on June 18, 2024, following the rigorous six-step framework proposed by Fernández-Sánchez to ensure a systematic and comprehensive evaluation of apps for HIV and STI prevention. Predefined inclusion and exclusion criteria were applied, resulting in 6 eligible apps. Each app was assessed using the 29-item Mobile App Rating Scale (MARS), scored on a 5-point Likert scale (1 = inadequate, 5 = excellent), and categorized as high (3), medium (2), or low (1) based on mean scores. Internal consistency was excellent (Cronbach's α = 0.90), and inter-rater reliability demonstrated near-perfect agreement (Cohen's κ = 0.862). Data analysis was performed using SPSS version 27. RESULTS: All six apps were available in Canada, Mexico, and the United States, with 33.3% from Google Play, 16.7% from Apple, and 50.0% from both platforms. MARS evaluation revealed high quality ratings for Engagement (100%), Functionality (88.9%), Aesthetics (83.3%), and Interaction (83.0%), as well as high subjective quality (83.3%) and app-specific quality (88.9%). Life4Me+ was the highest-rated app (4.6; 3/5), while HIV-TEST received the lowest rating (3.4; 7/5). Most apps (83.3%) were only available in English, and 16.7% supported multiple languages, which may limit accessibility for non-English-speaking migrant populations. Additionally, 83.3% were updated in 2024, 33.3% were linked to non-governmental organization, 16.7% to a university, and 50.0% had no clear affiliation. Regarding their focus, 50.0% addressed STI prevention, diagnosis, and treatment, 16.7% combined HIV and STI prevention, and 33.3% provided PrEP-related resources. CONCLUSIONS: These six apps stand out for their high functionality, engagement, and accessibility, establishing themselves as effective tools for HIV and STI prevention education among migrant populations. This study highlights the critical role of digital resources in addressing public health challenges faced by vulnerable and minority groups. Integrating these apps into health promotion strategies is essential to improve health literacy and encourage preventive behaviors. Moreover, ensuring the quality, credibility, linguistic diversity, and continuous updating of these digital interventions is crucial to achieving a real and sustained impact on public health. Policies should promote clear standards that guarantee accessibility, transparency, and accuracy, thereby facilitating access to healthcare services in complex migratory contexts.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".