What lies behind gender inequalities in HIV/AIDS in sub-Saharan African countries: evidence from Kenya, Lesotho and Tanzania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Within sub-Saharan Africa, women are disproportionately at risk for acquiring and having human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS). It is important to clarify whether gender inequalities in HIV prevalence in this region are explained by differences in the distributions of HIV risk factors, differences in the effects of these risk factors or some combination of both. We used an extension of the Blinder-Oaxaca decomposition approach to explain gender inequalities in HIV/AIDS in Kenya, Lesotho and Tanzania using data from the demographic and health and AIDS indicator surveys. After adjusting for covariates using Poisson regression models, female gender was associated with a higher prevalence of HIV/AIDS in Kenya [prevalence ratio (PR) = 1.73, 95% confidence interval (CI) = 1.33, 2.23 in 2003] and Lesotho (PR = 1.39, 95% CI = 1.20, 1.62 in 2004/05), but not in Tanzania. Decomposition analyses demonstrated two distinct patterns over time. In Tanzania, the gender inequality in HIV/AIDS was explained by differences in the distributions of HIV risk factors between men and women. In contrast, in Kenya and Lesotho, this inequality was partly explained by differences in the effects across men and women of measured HIV/AIDS risk factors, including socio-demographic characteristics (age and marital status) and sexual behaviours (age at first sex); these results imply that gender inequalities in HIV/AIDS would persist in Kenya and Lesotho even if men and women had similar distributions of HIV risk factors. The production of gender inequalities may vary across countries, with inequalities attributable to the unequal distribution of risk factors among men and women in some countries and the differential effect of these factors between groups in others. These different patterns have important implications for policies to reduce gender inequalities in HIV/AIDS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it