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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it