Co-Designing Digital Health Intervention for Monitoring Medication and Consultation Among Transgender People in Underserved Communities: Collaborative Approach
Bibliographic record
Abstract
BACKGROUND: In many parts of the world, men who have sex with men and transgender individuals face criminalization and discrimination. As a result, they are less likely to seek medical help, despite experiencing higher rates of HIV/AIDS, mental health issues, and other health problems. Reaching key populations (KPs) with essential testing, care, and treatment services can be challenging, as they often have a higher likelihood of contracting and spreading the virus. They have limited access to antiretroviral (ARV) therapy (ART) services, which means that KPs may continue to serve as reservoirs for new HIV infections if they do not receive effective HIV programming. This ongoing issue complicates efforts to control the epidemic. Therefore, modeling a digital health system to track ARV medication access and use is crucial. This paper advocates for the use of digital interventions to manage the health of KPs in underserved regions, using Nigeria as a case study. OBJECTIVE: This study aims to assess digital health interventions for monitoring medication and consultations among transgender people in underserved communities. It also sought to determine whether a system exists that could support ART adherence in Nigeria. Additionally, the study evaluated design strategies to address privacy and confidentiality concerns, aiming to reduce nonadherence to ARV medications among KPs in Nigeria. METHODS: A qualitative approach was adopted for this research, involving a thematic analysis of information collected from interviews with clinicians and other health practitioners who work directly with these communities, as well as from an interactive (virtual) workshop. RESULTS: The findings from the thematic analysis indicate a need to increase attendance at ART therapy sessions through the implementation of an intensive care web app. Unlike previous solutions, this study highlights the importance of incorporating a reminder feature that integrates with an in-app telemedicine consultancy platform. This platform would facilitate discussions about client challenges, such as adverse drug effects, counseling sessions with clinical psychologists, and the impact of identity discrimination on mental health. Other data-driven health needs identified in the study are unique drug request nodes, client-led viral load calculators, remote requests, and drug delivery features within the web app. Participants also emphasized the importance of monitoring medication compliance and incorporating user feedback mechanisms, such as ratings and encouragement symbols (eg, stars, checkmarks), to motivate adherence. CONCLUSIONS: The study concludes that technology-driven solutions could enhance ART adherence and reduce HIV transmission among transgender people. It also recommends that local governments and international organizations collaborate and invest in health management services that prioritize health needs over identity.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".