HIV self-testing relative to the landscape of HIV testing in Ontario, Canada
Bibliographic record
Abstract
BackgroundHIV self-testing may help achieve the UNAIDS 95-95-95 targets because it has the potential to increase testing among equity-denied communities. In 2023, the Public Health Agency of Canada made a one-time $8 million investment into HIV self-testing. We sought to evaluate the outcomes of HIV self-testing, compared to serology in Ontario, Canada.MethodsWe submitted data requests to all agencies involved HIV self-test distribution in Ontario, Canada for 2022-2023. We obtained matching data from the Public Health Ontario Laboratory. We then analyzed for unique test, unique tester, and positivity rate per testing modality.ResultsDuring the analysis period, we found that the laboratory completed an average of 53,606 tests per month for an average number of 44,671 unique persons. For self-tests, there was an average of 1700 tests distributed per month to an average of 678 unique persons. The positivity rate for self-testing was 0.27%, compared to 0.1% for serology.ConclusionsOur results highlight that self-testing can play a role but will not, alone, achieve the UNAIDS 95-95-95 targets. In our jurisdiction, self-testing corresponded with a higher positivity rate but accounted for only a minority of new diagnoses. In short, HIV self-testing is a tool, but not the solution to the HIV epidemic.
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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 | 0.003 |
| 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".