African, Caribbean, or Black participants report lower levels of STI/HIV risk but equal or higher rates of STI/HIV diagnoses: The GetaKit.ca study
Bibliographic record
Abstract
The COVID-19 pandemic and the HIV epidemic have both highlighted the need of race/ethnicity-based data to inform responses to infectious disease outbreaks. However, no such public health data exist in Canada. To generate some such data, we extracted data from the GetaKit.ca study, which is a website through which persons in Canada could obtain free HIV self-tests. We used data from April 1, 2021, to March 31, 2024. From 8,459 participants, of whom 16% ( n = 1240) identified as Black, we found that Black participants reported low levels of risk factors for STI/HIV acquisition. We also identified that Black compared to White participants reported lower rates of prior STI/HIV testing and prevention services, and lower overall rates of self-reported prior STI/HIV diagnoses, although this difference mainly only applied to prior chlamydia or gonorrhea infections among cis-male participants; there were no differences for the rates of self-reported prior syphilis infections (overall and in gay, bisexual, or other men who have sex with men) or chlamydia infections in cis women. Finally, diagnostic outcomes in the study identified nonsignificantly different rates of HIV diagnoses (from the HIV self-tests) but higher rates of chlamydia (from laboratory testing) among Black participants. These results highlight the need for more race/ethnicity-based data. They also suggest that current metrics of STI/HIV risk may not work well for Black populations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".