First-time testers in the GetaKit study: conceptualizing new paths to care for gbMSM
Bibliographic record
Abstract
When analyzing the data for Ontario, Canada, HIV rates continue to be highest among gay, bisexual and other men who have sex with men (gbMSM). Since HIV diagnosis is a key component of HIV care, self-testing has provided options for allowing this population to access care, resulting in a significant number of first-time testers. Between 1 April 2021 and 31 January 2022, 882 gbMSM participants ordered an HIV self-test through GetaKit. Of these, 270 participants reported that they had never undergone HIV testing previously. Our data showed that first-time testers were generally younger, members of BIPOC (Black, Indigenous and people of color) communities and they reported more invalid test results than those who had tested previously. This suggests that HIV self-testing may be a more successful and appealing component of the HIV prevention armamentarium for this population, but one that is not without its shortcomings as an entry to care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".