Income Status and Education as Predictors of HIV Transmission in South Africa
Bibliographic record
Abstract
The global Human Immunodeficiency Virus (HIV) epidemic targets various populations around the world, and South Africa is one of a number of countries where prevalence rates of the virus continue to increase despite the introduction of a viable treatment option. Previously investigated implications of HIV in South Africa are primarily related to its effects on the health-care sector of the country. However, complex socioeconomic processes are relevant to the discussion of HIV-related risk factors and consequences affecting individuals and households within South Africa. A large body of literature covers many socioeconomic perspectives on HIV, including the effect of socioeconomic status on HIV infection. While the roles of income status and education as risk factors for HIV infection have been explored extensively in a South African context, the connection between this and consequent adverse impacts on these factors as a result of HIV infection has not been clearly identified. This paper aims to address the gap in the literature regarding how specific socioeconomic factors act as risk factors for HIV contraction, but also how the same factors are affected as an associated outcome in those infected with HIV. Specifically, this paper argues that income status and education act as risk factors for HIV through their effects on individual behaviour, while also being adversely impacted due to the occurrence of infection. These impacts on income status and education contribute to South Africa’s inability to stop perpetuating the cycle of HIV prevalence.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".