Towards the elimination of mother-to-child transmission of HIV in Nigeria: a health system perspective of the achievements and challenges
Bibliographic record
Abstract
Despite its scaled-up response for prevention of mother-to-child transmission of HIV (PMTCT), Nigeria still contributes the greatest number of infants infected with HIV worldwide. Drawing on our knowledge, and review of policy documents and research papers, we explored the achievements and challenges in the elimination of mother-to-child transmission of HIV in Nigeria using the WHO's health systems framework. We found that Nigeria has increased the number of PMTCT sites, decentralized and integrated PMTCT care for expanded service delivery, adopted task-shifting to address the shortage of skilled healthcare providers, explored alternative sources of domestic funding to bridge the funding gap and harmonized the health management information system to improve data quality. Some of the challenges we identified included: difficulty in identifying HIV-infected pregnant women because of low uptake of antenatal care; interrupted supplies of medical commodities; knowledge gaps among healthcare workers; and lack of a national unique identifying system to enhance data quality. While there have been some achievements in the PMTCT program, gaps still exist in the different blocks of the health system. Elimination of mother-to-child transmission of HIV in Nigeria will require the implementation of feasible, culturally acceptable and sustainable interventions to address the health system-related challenges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".