Partner notification and treatment outcomes among South African adolescents and young adults diagnosed with a sexually transmitted infection via laboratory-based screening
Bibliographic record
Abstract
Partner notification and treatment are essential components of sexually transmitted infection (STI) management, but little is known about such practices among adolescents and young adults. Using data from a prospective cohort study (AYAZAZI) of youth aged 16–24 years in Durban, South Africa, we assessed the STI care cascade across participant diagnosis, STI treatment, partner notification, and partner treatment; index recurrent STI and associated factors; and reasons for not notifying partner of STI. Participants completed laboratory-based STI screening ( Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, Trichomonas vaginalis) at enrollment and at 12 months. Of the 37/216 participants with STI (17%), 27/37 (73%) were women and 10/37 (27%) were men. Median age was 19 years (IQR: 18–20). Of the participants with STI, 23/37 (62%) completed a Treatment and Partner Tracing Survey within 6 months of diagnosis. All survey participants reported completing STI treatment (100%), 17/23 (74%) notified a partner, and 6/23 (35%) reported partner treatment. Overall, 4/23 (11%) participants had 12-month recurrent C. trachomatis infection, with no association with partner notification or treatment. Stigma and lack of STI knowledge were reasons for not notifying partner of STI. STI partner notification and treatment is a challenge among youth. Novel strategies are needed to overcome barriers along the STI care cascade.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".