Implementation of couples’ voluntary HIV counseling and testing services in Durban, South Africa
Bibliographic record
Abstract
BACKGROUND: Couples' voluntary HIV counseling and testing (CVCT) is an evidence-based intervention that significantly reduces HIV incidence in couples. Despite the high prevalence of HIV and HIV couple serodiscordance in South Africa, there are few CVCT services. METHODS: From February-June 2013, The Rwanda Zambia HIV Research Group provided support, training, and technical assistance for local counselors and promoters to pilot CVCT services in five hospital-based clinics in Durban, South Africa. Client-level data (age, gender, years cohabiting, pregnancy status, previous testing, antiretroviral treatment (ART) status, neighborhood, and test site) collected as a component of routine CVCT service operation is presented stratified by couple serostatus. RESULTS: Twenty counselors and 28 promoters completed training. Of 907 couples (1,814 individuals) that underwent CVCT, prevalence of HIV was 41.8% and prevalence of HIV serodiscordance was 29.5% (19.3% M-F+, 10.3% M + F-). Most participants were 25-34 years of age, and this group had the highest prevalence. Previous individual HIV testing was low (50% for men, 63% for women). Only 4% of couples reported previous CVCT. Most (75%) HIV+ partners were not on ART, and HIV+ individuals in discordant couples were more likely to be on ART than those in concordant positive couples. Pregnancy among HIV+ women was not associated with previous HIV testing or ART use. CONCLUSIONS: Implementation of standard CVCT services was found to be feasible in Durban. The burden of HIV and couple serodiscordance in Durban was extremely high. CVCT would greatly benefit couples in Durban as an HIV prevention strategy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".