An Unstructured Supplementary Service Data System to Verify HIV Self-Testing Among Nigerian Youths: Mixed Methods Analysis of Usability and Feasibility
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) interventions among adolescents and young adults (AYAs) are increasingly available in African low- and middle-income countries (LMICs). For example, the unstructured supplementary service data (USSD) could be used to verify HIV self-testing (HIVST) among AYAs with poor bandwidth. OBJECTIVE: The aim of this study is to describe the creation of an USSD platform and determine its feasibility and usability to promote the verification of HIVST results among AYAs in Nigeria. METHODS: We developed and evaluated a USSD platform to verify HIVST results using a user-centered approach. The USSD platform guided AYAs in performing HIVST, interpreting the result, and providing linkage to care after the test. Following the usability assessment, the USSD platform was piloted. We used a mixed methods study to assess the platform's usability through a process of quantitative heuristic assessment, a qualitative think-aloud method, and an exit interview. Descriptive statistics of quantitative data and inductive thematic analysis of qualitative variables were organized. RESULTS: A total of 19 AYAs participated in the usability test, with a median age of 19 (IQR 16-23) years. There were 11 females, 8 males, and 0 nonbinary individuals. All individuals were out-of-school AYAs. Seven of the 10 Nielsen usability heuristics assessed yielded positive results. The participants found the USSD platform easy to use, preferred the simplicity of the system, felt no need for a major improvement in the design of the platform, and were happy the system provided linkage to care following the interpretation of the HIVST results. The pilot field test of the platform enrolled 164 out-of-school AYAs, mostly young girls and women (101, 61.6%). The mean age was 17.5 (SD 3.18) years, and 92.1% (151/164) of the participants reported that they were heterosexual, while 7.9% (13/164) reported that they were gay. All the participants in the pilot study were able to conduct HIVST, interpret their results, and use the linkage to care feature of the USSD platform without any challenge. A total of 7.9% (13/164) of the AYAs had positive HIV results (reactive to the OraQuick kit). CONCLUSIONS: This study demonstrated the usability and feasibility of using a USSD system as an alternative to mobile phone apps to verify HIVST results among Nigerian youth without smartphone access. Therefore, the use of a USSD platform has implications for the verification of HIVST in areas with low internet bandwidth. Further pragmatic trials are needed to scale up this approach.
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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 | 0.053 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".