P4.20 Factors associated with opting in for clinic-based syphilis testing among transgender women in jamaica
Bibliographic record
Abstract
Introduction Transgender women experience high sexually transmitted infection (STI) rates, yet there is a lack of information about STI testing uptake among transgender women in low and middle-income countries. We conducted a tablet-based survey to assess syphilis testing uptake and prevalence among transgender women in Jamaica. Methods We conducted a cross-sectional survey with a peer-driven recruitment sample of transgender women in Kingston and Ocho Rios, Jamaica. Participants were provided with a coupon with their survey identification (ID) code for voluntary, free, rapid serological syphilis testing. Coupon ID codes for testing uptake/results were linked with survey results. We conducted backwards stepwise logistic regression to determine factors associated with opting in for syphilis testing. Results Among 137 participants (mean age: 24.0 [SD: 4.5]), 60.6% opted in for syphilis testing and 10.6% tested positive. One-quarter (25.2%) self-reported being HIV-positive; all participants with syphilis infection were HIV-positive. In univariable analyses having multiple partners was associated with reduced odds of opting in for testing (OR: 0.19; 95% CI: 0.06–0.60). In multivariable analyses controlling for relationship status, HIV-positive participants were four-fold more likely to opt-in for syphilis testing (Adjusted Odds Ratio [AOR: 4.33]; 95% CI: 1.31–14.26) than HIV-negative participants. Perceived STI risk (AOR: 1.58; 95% CI: 1.04–2.40) and childhood sexual abuse history (AOR: 2.80; 95% CI: 1.03–7.62) were associated with increased odds of opting in for testing. Incarceration history (AOR: 0.27; 95% CI: 0.11–0.71) was associated with reduced odds of opting in for syphilis testing. Conclusion Transgender women in Jamaica experience high HIV and syphilis prevalence, and syphilis and HIV co-infection. Findings suggest opt-in clinic based syphilis testing may miss the opportunity to provide testing for some transgender women at elevated STI risk. Future research should assess whether point-of-care syphilis testing may increase testing uptake.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".