Towards Comprehensive Women's Healthcare in Sub-Saharan Africa
Bibliographic record
Abstract
This themed supplement to JAIDS: Journal of Acquired Immune Deficiency Syndromes focuses on the critical intersections between HIV, reproductive, and maternal health services in the health systems of sub-Saharan Africa. The epidemiology of HIV among women of reproductive age on the sub-continent demands a holistic conceptualization and comprehensive approaches to ensure that HIV, reproductive, and maternal health are optimally addressed. Yet, in many instances, the national and global responses to these health issues remain siloed. Women's health needs and new global and national guidelines for HIV treatment raise important policy, programmatic, and operational questions regarding service integration, scale-up, and health systems functioning. In June 2013, the Maternal Health Task Force at the Harvard School of Public Health, the United States Agency for International Development, and the United States Centers for Disease Control and Prevention convened an international technical meeting of researchers, policymakers, and practitioners to discuss the existing evidence base about the interconnections between HIV, reproductive, and maternal health and identify the most important knowledge gaps and research priorities. The articles in this special issue deepen and expand on those discussions by (1) providing empirical evidence about challenges, (2) identifying how improving clinical care and models of service delivery, strengthening health systems, and addressing social dynamics can contribute to better outcomes, and (3) mapping future research directions. Together, these articles underscore that new policy frameworks and integrated approaches are necessary but not sufficient to address health system challenges. Addressing the multiple needs of women of reproductive age who are living with HIV or are at risk of acquiring HIV is a complex undertaking that requires improved access to, utilization and quality of comprehensive women's healthcare. Continued evaluation and knowledge generation are needed to ensure that potential health gains are actualized.
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.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".