Childbearing Intentions of HIV-Positive Women of Reproductive Age in Soweto, South Africa: The Influence of Expanding Access to HAART in an HIV Hyperendemic Setting
Bibliographic record
Abstract
OBJECTIVES: We investigated whether the intention to have children varied according to HIV status and use of highly active antiretroviral therapy (HAART) among women in Soweto, South Africa. METHODS: We used survey data from 674 women aged 18 to 44 years recruited from the Perinatal HIV Research Unit in Soweto (May through December 2007); 217 were HIV-positive HAART users (median duration of use = 31 months; interquartile range = 28, 33), 215 were HIV-positive and HAART-naive, and 242 were HIV negative. Logistic regression models examined associations between HIV status, HAART use, and intention to have children. RESULTS: Overall, 44% of women reported intent to have children, with significant variation by HIV status: 31% of HAART users, 29% of HAART-naive women, and 68% of HIV-negative women (P < .001). In adjusted models, HIV-positive women were nearly 60% less likely to report childbearing intentions compared with HIV-negative women (for HAART users, adjusted odds ratio [AOR] = 0.40; 95% confidence interval [CI] = 0.23, 0.69; for HAART-naive women, AOR = 0.35; 95% CI = 0.21, 0.60), with minimal differences according to use or duration of HAART. CONCLUSIONS: Integrated HIV, HAART, and reproductive health services must be provided to support the rights of all women to safely achieve their fertility goals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".