Development of an mHealth App by Experts for Queer Individuals’ Sexual-Reproductive Health Care Services and Needs: Nominal Group Technique Study
Bibliographic record
Abstract
BACKGROUND: Queer individuals continue to be marginalized in South Africa; they experience various health care challenges (eg, stigma, discrimination, prejudice, harassment, and humiliation), mental health issues (eg, suicide and depression), and an increased spread of HIV or AIDS and sexually transmitted illnesses (STIs; chlamydia, gonorrhea, and syphilis). Mobile health (mHealth) apps have the potential to resolve the health care deficits experienced by health care providers when managing queer individuals and by queer individuals when accessing sexual-reproductive health care services and needs, thus ensuring inclusivity and the promotion of health and well-being. Studies have proven that the nominal group technique (NGT) could be used to solve different social and health problems and develop innovative solutions. This technique ensures that different voices are represented during decision-making processes and leads to robust results. OBJECTIVE: This study aims to identify important contents to include in the development of an mHealth app for addressing the sexual-reproductive health care services and needs of queer individuals. METHODS: We invited a group of 13 experts from different fields, such as researchers, queer activists, sexual and reproductive health experts, private practicing health care providers, innovators, and private health care stakeholders, to take part in a face-to-face NGT. The NGT was conducted in the form of a workshop with 1 moderator, 2 research assistants, and 1 principal investigator. The workshop lasted approximately 2 hours 46 minutes and 55 seconds. We followed and applied 5 NGT steps in the workshop for experts to reach consensus. The main question that experts were expected to answer was as follows: Which content should be included in the mHealth app for addressing sexual-reproductive health care services and needs for queer individuals? This question was guided by user demographics and background, health education and information, privacy and security, accessibility and inclusivity, functionality and menu options, personalization and user engagement, service integration and partnerships, feedback and improvement, cultural sensitivity and ethical considerations, legal and regulatory compliance, and connectivity and data use. RESULTS: Overall, experts voted and ranked the following main icons: menu options (66 points), privacy and security (39 points), user engagement (27 points), information hub (26 points), user demographics (20 points), connectivity (16 points), service integration and partnerships (10 points), functionalities (10 points), and accessibility and inclusivity (7 points). CONCLUSIONS: Conducting an NGT with experts from different fields, possessing vast skill sets, knowledge, and expertise, enabled us to obtain targeted data on the development of an mHealth app to address sexual-reproductive health care services and needs for queer individuals. This approach emphasized the usefulness of a multidisciplinary perspective to inform the development of our mHealth app and demonstrated the future need for continuity in using this approach for other digital health care innovations and interventions.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".