Comparing the Uptake of HIV Self-Testing to HIV Serology: Findings from GetaKit—A Prospective Open Cohort Study in Ontario, Canada
Bibliographic record
Abstract
BackgroundHIV self-tests (HIVSTs) have been promoted as one way to increase testing.MethodsWe extracted data from the GetaKit study for October 11, 2023 to June 30, 2025, focusing on participants to whom we co-offered an HIVST and serology.ResultsWe co-offered HIVST and serology to 3611 persons; 71.9% agreed to go to a lab and 19.4% opted for only the HIVST. Participants who were Black, Indigenous, or Persons of Color were less willing to attend a lab; participants who were men who have sex with men or reported injection drug use or sex work were more willing to attend a lab. First-time testers opted for the HIVST at a higher rate. HIVST did not yield new diagnoses.ConclusionsHIVSTs were an entry point to testing for some but were not the preferred modality for most. Promoting HIVSTs too broadly would not align with patient preference.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".