HIV self-testing: what GetaKit can tell us about Canada’s $8 million one-time investment
Bibliographic record
Abstract
International AIDS Conference in Montreal, Canada's Federal Health Minister announced that the Government of Canada will invest $17 million to increase access to HIV testing, $8 million of which would be used to purchase and distribute HIV self-tests. While HIV testing, and subsequent diagnoses, is a critical first step to achieving the updated UNAIDS goals of 95-95-95, testing on its own does not guarantee linkage to treatment or prevention services. In other words, it does not alone guarantee progress toward the 95-95-95 goals. GetaKit, Canada's first HIV self-test mail-out project, has demonstrated that a preliminary risk-assessment consistent with US CDC and PHAC screening guidelines ensures targeted uptake among communities most affected by HIV, thus minimizing the risk of false positive results and poor positive predictive values. Furthermore, HIV self-testing must link not only individuals with positive results to treatment, but also persons with negative results to pre-exposure prophylaxis (PrEP) along with re-testing as required. However, both access to treatment and PrEP remain inconsistently available across Canada. Therefore, while this one-time investment of funding to increase HIV testing is encouraging, without clear instructions as to who should be prioritized for testing and definitive next steps to ensure that individuals are successfully linked to care, Canada risks wasting resources, further exacerbating pre-existing inequities.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".