At-home HIV self-testing during COVID: implementing the GetaKit project in Ottawa
Bibliographic record
Abstract
SETTING: In March 2020, COVID-19 shuttered access to many healthcare settings offering HIV testing and there is no licensed HIV self-test in Canada. INTERVENTION: A team of nurses at the University of Ottawa and Ottawa Public Health and staff from the Ontario HIV Treatment Network (OHTN) obtained Health Canada's Special Access approval on April 23, 2020 to distribute bioLytical's INSTI HIV self-test in Ottawa; we received REB approval on May 15, 2020. As of July 20, 2020, eligible participants (≥18 years old, HIV-negative, not on PrEP, not in an HIV vaccine trial, living in Ottawa, no bleeding disorders) could register via www.GetaKit.ca to order kits. OUTCOMES: In the first 6 weeks, 637 persons completed our eligibility screener; 43.3% (n = 276) were eligible. Of eligible participants, 203 completed a baseline survey and 182 ordered a test. These 203 participants were an average of 31 years old, 72.3% were white, 60.4% were cis-male, and 55% self-identified as gay. Seventy-one percent (n = 144) belonged to a priority group for HIV testing. We have results for 70.9% (n = 129/182) of participants who ordered a kit: none were positive, 104 were negative, 22 were invalid, and 2 "preferred not to say"; 1 participant reported an unreadiness to test. IMPLICATIONS: Our results show that HIV self-testing is a pandemic-friendly strategy to help ensure access to sexual health services among persons who are good candidates for HIV testing. It is unsurprising that no one tested positive for HIV thus far, given the 0.08% positivity rate for HIV testing in Ottawa. As such, we advocate for scale-up of HIV self-testing in Canada.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".