Eliminating Preventable HIV-Related Maternal Mortality in Sub-Saharan Africa
Bibliographic record
Abstract
INTRODUCTION: HIV makes a significant contribution to maternal mortality, and women living in sub-Saharan Africa are most affected. International commitments to eliminate preventable maternal mortality and reduce HIV-related deaths among pregnant and postpartum women by 50% will not be achieved without a better understanding of the links between HIV and poor maternal health outcomes and improved health services for the care of women living with HIV (WLWH) during pregnancy, childbirth, and postpartum. METHODS: This article summarizes priorities for research and evaluation identified through consultation with 30 international researchers and policymakers with experience in maternal health and HIV in sub-Saharan Africa and a review of the published literature. RESULTS: Priorities for improving the evidence about effective interventions to reduce maternal mortality and improve maternal health among WLWH include better quality data about causes of maternal death among WLWH, enhanced and harmonized program monitoring, and research and evaluation that contributes to improving: (1) clinical management of pregnant and postpartum WLWH, including assessment of the impact of expanded antiretroviral therapy on maternal mortality and morbidity, (2) integrated service delivery models, and (3) interventions to create an enabling social environment for women to begin and remain in care. CONCLUSIONS: As the global community evaluates progress and prepares for new maternal mortality and HIV targets, addressing the needs of WLWH must be a priority now and after 2015. Research and evaluation on maternal health and HIV can increase collaboration on these 2 global priorities, strengthen political constituencies and communities of practice, and accelerate progress toward achievement of goals in both areas.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".