Targeted HIV Self-Testing Identifies Persons with Undiagnosed HIV and Active Engagement Links them to Care: The GetaKit Study
Bibliographic record
Abstract
Current international HIV testing guidelines recommend that HIV negative persons from HIV priority groups complete repeat screening every 3-6 months; local guidelines in our jurisdiction recommend that such retesting should occur every 3 months. Such an approach allows for timely HIV diagnosis and linkage to care - and aligns with the UNAIDS 95-95-95 targets to have 95% of undiagnosed persons be aware of their HIV status. To meet these aims, new approaches to HIV testing have been developed, including our HIV self-testing initiative, GetaKit.ca, which uses an online screening algorithm to determine eligibility and has built in pathways for re-test reminders, linkage HIV prevention care, and rapid follow-up for positive test results. To understand self-testing frequency in relation to our local recommendations for resting every 3 months, we evaluated data from participants who ordered repeat HIV self-tests through GetaKit.ca. Descriptive analyses were performed on participant characteristics and chi-square tests were performed on aggregated participant risk data. During the study period, 5235 HIV self-tests were distributed to 3627 participants, of whom, 26% ordered more than once and 27% belonged to an HIV priority population. Participants who retested were more likely to have been white, male, and part of an HIV priority population; they were also more likely to have completed prior STI or HIV testing or had a prior STI diagnosis, compared to those who did not. We identified 16 new HIV diagnoses, 2 of which were among repeat testers. Our results suggest that HIV self-testing can be useful to help meet UNAIDS targets to identify undiagnosed infections; however, such efforts are less likely to be successful without adequate linkage to follow-up services, including HIV treatment and prevention care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".