HIV self‐testing enabled access to testing for Black persons: The GetaKit study
Bibliographic record
Abstract
In Ontario, new HIV diagnoses continue to affect gay, bisexual, and other men who have sex with men (gbMSM) and persons of African, Caribbean, or Black (ACB) ethnicities. Because treatment and suppressed viral loads flow from diagnosis, testing is key. We sought to determine the outcomes of online ordering and mail-out of free at-home HIV self-testing in Ontario, Canada. We implemented the GetaKit study to offer such free HIV self-testing using the INSTI® test and offered it via a website (GetaKit.ca) to eligible persons in Ontario. From April 1, 2021 to January 31, 2022, we distributed kits to 1551 persons; 40% were white and 26% were ACB. We found that ACB participants were less likely to fulfill established criteria for HIV PrEP, such as previous sexually transmitted infections diagnoses and drug use, despite having a similar test positivity rate. We also found that ACB women, who may represent a larger number of new infections than previously thought, did not have a higher rate of first-time testing. Our results suggest that HIV self-testing can enable testing, but that work is required to increase uptake among ACB persons and women. Overall, HIV self-testing thus corresponded with increased testing among persons who were Black, yet lower rates of results reporting. Patient or Public Contributions: Community members from the AIDS Committee of Ottawa, Max Ottawa, and Black Cap, and nurses from Ottawa Public Health were involved in the design, promotion, and implementation of this study.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".