Social Protection and HIV risk Factors among Youth in Southern Africa: An Analysis of Cross-sectional Population-based HIV Impact Assessment Surveys
Bibliographic record
Abstract
Poverty fuels risky sexual behaviors associated with HIV infection among youth. Interventions like cash transfers may mitigate HIV risk. We explored the role of broader social protection (including food, educational, and social transfers) in reducing HIV risk among 15-24-year-olds in Southern Africa. We analyzed Population-based HIV Impact Assessment surveys data from 31,317 youth in eSwatini, Lesotho, Malawi, Namibia, Zambia, and Zimbabwe (2015-2017). Using inverse probability-weighted multivariable logistic regression, we examined associations between types of social protection and condomless sex, multiple partnerships, and high-risk sexual behaviors. Food support was associated with reduced odds of condomless sex (OR 0.71 [95% CI 0.61-0.82]), multiple partnerships (0.77 [0.63-0.95]), and high-risk sex (0.70 [0.60-0.82]). Educational support was associated with reduced odds of condomless sex (0.57 [0.46-0.59]) and high-risk sex (0.59 [0.47-0.73]). Social transfers were associated with reduced odds of condomless sex (0.62 [0.54-0.70]) and high-risk sex (0.50 [0.44-0.56]). The benefits of social protection varied across countries. Educational support was associated with reduced odds of any HIV risk factors in eSwatini, Zambia, and Zimbabwe. However, the protective effect of social transfers was only observed in eSwatini, and the benefit of food support was only significant in Namibia. Furthermore, protective associations were more pronounced among females than males. This study underscores the potential of social protection to strengthen HIV prevention efforts by mitigating poverty-related risk factors, particularly for adolescent girls and young women in Southern Africa. The impact of specific programs appears context-dependent, highlighting the need for tailored interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".