Antiretroviral Therapy Helps HIV-Positive Women Navigate Social Expectations for and Clinical Recommendations against Childbearing in Uganda
Bibliographic record
Abstract
Understanding factors that influence pregnancy decision-making and experiences among HIV-positive women is important for developing integrated reproductive health and HIV services. Few studies have examined HIV-positive women's navigation through the social and clinical factors that shape experiences of pregnancy in the context of access to antiretroviral therapy (ART). We conducted 25 semistructured interviews with HIV-positive, pregnant women receiving ART in Mbarara, Uganda in 2011 to explore how access to ART shapes pregnancy experiences. Main themes included: (1) clinical counselling about pregnancy is often dissuasive but focuses on the importance of ART adherence once pregnant; (2) accordingly, women demonstrate knowledge about the role of ART adherence in maintaining maternal health and reducing risks of perinatal HIV transmission; (3) this knowledge contributes to personal optimism about pregnancy and childbearing in the context of HIV; and (4) knowledge about and adherence to ART creates opportunities for HIV-positive women to manage normative community and social expectations of childbearing. Access to ART and knowledge of the accompanying lowered risks of mortality, morbidity, and HIV transmission improved experiences of pregnancy and empowered HIV-positive women to discretely manage conflicting social expectations and clinical recommendations regarding childbearing.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".