Partner Notification Approaches for Sex Partners and Children of Human Immunodeficiency Virus Index Cases in Côte d'Ivoire
Bibliographic record
Abstract
BACKGROUND: Four partner notification approaches were introduced in health facilities in Côte d'Ivoire to increase human immunodeficiency virus (HIV) testing uptake among the type of contacts (sex partners and biological children younger than 15 years). The study assessed the 4 approaches: client referral (index cases refer the contacts for HIV testing), provider referral (health care providers refer the contacts), contract referral (index case-provider hybrid approach), and dual referral (both the index and their partner are tested simultaneously). METHODS: Program data were collected at 4 facilities from October 2018 to March 2019 from index case files and HIV testing register. We compared uptake of the approaches, uptake of HIV testing, and HIV positivity percentages, stratified by contact type and gender. RESULTS: There were 1089 sex partners and 469 children from 1089 newly diagnosed index cases. About 90% of children were contacted through client referral: 85.2% of those were tested and 1.4% was positive. Ninety percent of the children came from female index cases. The provider referral brought in 56.3% of sex partners, of whom 97.2% were HIV-tested. The client referral brought in 30% of sex partners, of whom only 81.5% were HIV-tested. The HIV positivity percentages were 75.5% and 72.7%, respectively, for the 2 approaches. Male index cases helped to reach twice as many HIV-positive sexual contacts outside the household (115) than female index cases (53). The contract and dual referrals were not preferred by index cases. CONCLUSIONS: Provider referral is a successful and acceptable strategy for bringing in sex partners for testing. Client referral is preferred for children.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".