Unprotected sex following HIV testing among women in Uganda and Zimbabwe: short- and long-term comparisons with pre-test behaviour
Bibliographic record
Abstract
BACKGROUND: Despite widespread condom promotion for HIV prevention, prospective measurement of condom use before and after HIV testing is infrequent. METHODS: We analysed data from a prospective study of hormonal contraception and HIV acquisition among Zimbabwean and Ugandan women (1999-2004), in which HIV testing and counselling were performed approximately every 3 months. We used zero-inflated negative binomial (ZINB) models to examine the number and proportion of unprotected sex acts, comparing behaviour reported 2-6 months before HIV testing with behaviour reported both 2-6 months (short-term analysis) and 12-16 months (long-term analysis) after HIV testing. RESULTS: Short- and long-term analyses were similar, so we present only long-term findings from 151 HIV-infected and 650 uninfected participants. The proportion of HIV-infected women reporting any unprotected acts in a typical month declined from 74% (pre-infection behaviour) to 56% (12-16 months after HIV diagnosis). In multivariable models, HIV-infected women were twice as likely to report that all sex acts were protected by condoms after diagnosis compared with beforehand [adjusted odds ratio (aOR): 1.99, 95% confidence interval (CI): 1.12-3.53]; uninfected women were somewhat less likely to report that all acts were protected (aOR: 0.82, 95% CI: 0.64-1.04). HIV-infected women also reduced their number of unprotected acts by 38% (95% CI: -16 to -55%). However, their proportion of unprotected acts changed little (7% reduction, 95% CI: -18 to + 6%). Uninfected women reported little change in number or proportion of unprotected acts over the same time period. CONCLUSIONS: HIV-infected women reduced the number, but not the proportion, of unprotected acts. HIV-negative women did not increase condom use after testing and counselling, but neither did they decrease condom use, suggesting that testing negative was not interpreted as endorsement of risky behaviour.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".