The 4 Youth By Youth mHealth Photo Verification App for HIV Self-testing in Nigeria: Qualitative Analysis of User Experiences
Bibliographic record
Abstract
BACKGROUND: Despite the global expansion of HIV self-testing (HIVST), many research studies still rely on self-reported outcomes. New HIVST verification methods are needed, especially in resource-limited settings. OBJECTIVE: This study aims to evaluate the user experience of a mobile health (mHealth) app to enhance HIVST result reporting and verification. METHODS: Semistructured, in-depth interviews were used to evaluate the user experience of the 4 Youth By Youth mHealth photo verification app for HIVST. We used a think-aloud approach, and participants performed usability tasks and completed a qualitative exit interview. The app included HIV educational resources, step-by-step video instructions for performing HIVST, a 20-minute timer, a guide on interpreting results with linkages to care, an offline version, and a photo verification system. Demographic characteristics were reported by using descriptive statistics. Qualitative data were analyzed by using thematic analysis. RESULTS: A total of 19 users-12 women and 7 men-with a mean age of 22 years, participated in the study. The users completed the usability tasks and successfully uploaded a photo of their test results by using the app without assistance. Four main themes were identified in the data. First, in terms of user-friendly design, the participants noted the user-friendly features of the offline version and the app's low data use. However, some wanted the app to work in the background when using their mobile phone, and the font used should be more youth friendly. Second, in terms of ease of use, participants remarked that the app's self-explanatory nature and instructions that guided them on how to use the app enhanced its use. Third, in terms of a user's privacy, many participants reinforced the importance of privacy settings and tools that protect confidentiality among users. Finally, in terms of linkage to care, participants noted that the app's linkage to care features were useful, particularly in relation to referrals to trained counselors upon the completion of the test. All the participants noted that the app provided a convenient and private means of verifying the HIV test results. CONCLUSIONS: Our findings demonstrated the importance of engaging end users in the development phase of health technology innovations that serve youth. Clinical trials are needed to determine the efficacy of using an mHealth app to verify HIVST results among young people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".