Factors associated with time to presentation to Sydney Sexual Health Centre, Australia, after STI contact notification
Bibliographic record
Abstract
Background Despite partner notification (PN) being an essential component of sexually transmitted infection (STI) control programs, little is known about how contacts of STIs are notified, and the time taken to present for testing. We aimed to evaluate both aspects in people presenting to Sydney Sexual Health Centre who reported being a sexual contact of someone diagnosed with an STI. Methods We conducted a retrospective observational study of data collected between 1 April 2020 and 31 March 2021 at Sydney Sexual Health Centre. A pop-up field in the electronic medical record collected data about people's experience of being notified of their sexual contact with an STI. We ran univariable and multivariable analysis of time to presentation and PN method against clinical and demographic information. Results There were 2182 presentations because of STI contact notification. Median time to presentation was 3days (IQR 1-7days), which did not differ by spoken or electronic PN. In the multivariable model, people who received spoken PN were less likely to present in P =0.007). This indicates electronic PN may prompt faster testing for STIs. Higher partner number was associated with receiving electronic PN. Conclusions Our study suggests that electronic PN may prompt faster testing for STIs. It provides valuable insights into the characteristics of STI contacts, who are rarely the focus of PN research. Understanding what motivates notified contacts to seek care is integral in modernising PN practices, as rates of STIs climb.
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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 | 0.009 |
| 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) | 0.005 | 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".