P5.025 Development and Comparative Evaluation of an Innovative HIV Self-Testing Smartphone Application, an Internet-Based and a Paper-Based Instructional Programme in South Africa
Bibliographic record
Abstract
Background South Africa has about 11% of the total population living with HIV, the largest to date for any country. Facility-based HIV testing has reached only 50% South Africans because of fear of visibility leading to stigma, embarrassment and discrimination. Alternative strategies like self-testing for HIV may improve engagement, but evidence is limited. For self-testing to be successful, knowledge regarding the process, clear instructions about how to conduct, interpret and seek linkages to counselling and staging is essential. Methods We created an internet-based HIV self-testing programme with a popular oral HIV test. The programme had built-in content for counselling, personal risk staging, instructions to self-test, and to seek counselling and referral. We also created an equivalent paper version and evaluated both programmes in 251 health care professionals working at University of Cape Town, South Africa. The tested internet programme was converted into an interactive, engaging smartphone HIV self-test application. The application was piloted for design, content and comprehension in 12 young adults (aged 18–25 years). Results Internet and paper-based self-testing programmes were well received (91.3%) by participants with overall preference for self-testing reported at 100%. User feedback on the smartphone application was incorporated after pilot evaluation and the following were improved: (a) a user centred design and layout, (b) colourful interface with clear instructions, (c) clarity of content for comprehension, (d) built-in features for expanded access, and (e) overall presentation. After six iterations, a prototype Android application was developed. Conclusion High preference to self-test facilitated the use of the internet and paper-based programmes. This indicates that if validated self-tests are presented with clear instructions to self-test and built-in confidential linkages to counselling and treatment are provided, many more individuals will opt for HIV self-testing. These programmes and the smartphone application will be useful for the scale-up of unsupervised self-testing initiatives in literate populations worldwide.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".