Development of an mHealth App Prototype for LGBTQIA+ Individuals’ Sexual and Reproductive Health in Gauteng Province, South Africa: Design Science Research Study
Bibliographic record
Abstract
Background: The fast rate of technological advances in the health care sector remain as a pressing need for effective solutions that address the unique health care needs of sexual and gender minorities. If these innovative solutions are considered, societal challenges such as stigma, discrimination, and a lack of tailored health care resources, as experienced by the lesbian, gay, bisexual, transgender, queer, intersex, asexual, and more (LGBTQIA+) individuals could be addressed at lower cost. Objective: This study aimed to develop a mobile health (mHealth) app specifically designed to address the sexual and reproductive health (SRH) of lesbian, gay, bisexual, transgender, queer, intersex, asexual, and more (LGBTQIA+) individuals in Gauteng Province, South Africa. Methods: This study used a Design Science Research (DSR) framework and a mixed-method exploratory sequential approach. DSR was executed in three cycles: cycle one followed an ethnography approach; involved 33 health care providers (HCPs) and 22 LGBTQIA+ individuals, focusing on identifying specific SRH and the challenges encountered in accessing and providing these services. Participants shared their views on the potential role of mHealth apps in addressing these issues. Cycle two engaged 13 experts through Participatory Action Research (PAR) approach using the Nominal Group Technique (NGT) to collaboratively identify essential content for the app, fostering a co-creation process. Lastly, cycle three followed an interventional pre-experimental approach by involving software developers and principal investigator working together to develop a functional prototype of the mHealth app. Results: This study revealed critical insights into the specific SRH of LGBTQIA+ individuals, alongside the barriers faced by health care providers in meeting these needs. The co-created app prototype named "Queery wellness hub" was developed and incorporated features tailored to enhance accessibility, confidentiality, and user engagement, addressing both user and provider perspectives. Conclusions: The findings underscore the potential of mHealth apps in transforming the delivery of SRH for LGBTQIA+ individuals in South Africa. Continued collaboration with stakeholders is essential for further refinement and successful implementation of the app, ultimately contributing to better health outcomes for sexual and gender minorities.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".