An Unsupervised Smart App–Optimized HIV Self-Testing Program in Montreal, Canada: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Although HIV self-testing strategies have been recommended by the World Health Organization, HIV self-tests are not yet approved in Canada. Currently approved HIV self-tests offer toll-free lines that are insufficient for initiating expedited linkages to counseling and care, accurate interpretation, and support during HIV self-testing. We developed an innovative, multilingual software app called HIVSmart! to plug these gaps. OBJECTIVE: This study aimed to test our app-optimized oral HIV self-testing strategy for feasibility in men who have sex with men (MSM) who presented to test at a large sexual health clinic (Clinique Médicale L'Actuel) in Montreal. METHODS: Between July 2016 and February 2017, we offered a strategy consisting of the OraQuick In-Home HIV Test (an investigational device) and a tablet installed with the HIVSmart! app to study participants, who presented at a private office in the clinic, mimicking an unsupervised home environment. We evaluated the strategy for its feasibility, acceptability, and preference. Using the HIVSmart! app, participants were guided through the self-testing process. We determined feasibility with a metric defined as the completion rate, which consisted of the following 3 steps: (1) self-test conduct; (2) self-test interpretation; and (3) linkages to care. Participants independently performed, interpreted, recorded their self-test and result, engaged in pre- and posttest counseling, and sought linkages to care. Laboratory tests (p24, Western Blot, and RNA), as per country algorithms, were expedited, and linkages based on the rapid test status were arranged. RESULTS: Mean age of the 451 participants enrolled was 34 (range, 18-73) years. Of all participants, 97.1% (438/451) completed and submitted the survey through the HIVSmart! app. In total, 84.7% (371/438) of the participants were well educated (beyond high school) and 52.5% (230/438) had been tested within the past 6 months. Of the 451, 11.5% (52/451) were on pre-exposure prophylaxis. Feasibility (completion rate), an average proportion of the 3 steps, was computed to be 96.6% (419/451). The acceptability of the strategy was high at 98.5% (451/458). A majority of the participants (448/451, 99.3%) were found to be self-tested and lab-confirmed negative and were counseled after self- and rapid tests. In total, 0.7% (3/451) of the participants who self-tested positive and were lab-confirmed positive were linked to a physician within the same day. Furthermore, 98.8% (417/422) of the participants found the app to be useful and 94.0% (424/451) were willing to recommend it to a friend or partner. CONCLUSIONS: The HIVSmart! app-optimized strategy was feasible, accepted, and preferred by an educated, urban MSM population of Montreal. With the app, participants were able to perform, interpret, store results, and get rapidly linked to care. The HIVSmart!-optimized, self-testing strategy could be adapted and contextualized to many at-risk populations within Canada and worldwide, thereby maximizing its public health impact.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".