HIV Risks in Sexual Networks of Heterosexual Men in South Africa
Bibliographic record
Abstract
BACKGROUND: The interaction of HIV risks in sexual networks remains unclear in South Africa. We provide an overview of the dynamics of HIV risks in South African men through a systematic scoping review. METHODS & ANALYSIS: Literature searches were conducted on seven online databases. Two reviewers independently screened articles against the inclusion criteria and performed a Kappa coefficient test to evaluate the degree of agreement on article selection. Thematic content analysis and a Mixed Method Appraisal Tool version 2018 were used to present the narrative account of the outcomes and to assess the risk of bias on included studies. RESULTS: Of the 1356 records identified, six studies reported on the dynamics of HIV infection in heterosexual men in sexual networks. All studies that were included were published between 2006 and 2016. The participants were aged 13 years and above and comprised of sero-discordant couples, HIV patients, and male and female in the general population. These studies were conducted in multiple diverse regions including South Africa, Senegal, Uganda, Malawi, Kenya, Tanzania, Botswana and Zambia. Evidence showed that age and sexual partnerships were most commonly identified attributes to either HIV infection and/or transmission risks in men. While other biological and behavioral data were reported, the results were not specific to men. DISCUSSION: The impact of age and sexual partnerships are poorly understood and the data available limit inferences to South African men. Limited empiric evidence of HIV risk among men impacts on the design, development and tailoring of HIV prevention interventions to alter the trajectory.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".