Scaling up access to antiretroviral treatment for HIV: lessons from a key populations program in Nigeria
Bibliographic record
Abstract
Over the years, Nigeria has recorded significant progress in controlling the HIV epidemic in the country. HIV prevalence has reduced from 4.1% in 2010 to 1.4 in 2019. The number of people acquiring new HIV infections decreased from 120,000 in 2010 to 74,000 in 2021, and HIV-related deaths decreased from 82,000 in 2010 to 51,000 in 2021. However, the country still faces challenges such as high HIV transmission among key populations (KP) who account for 11% of new HIV infections. Over the years, the government and development partners involved in HIV response efforts in Nigeria have been establishing and scaling up access to services to help address the needs of KPs. Initially, services for KPs as with the general population in Nigeria were largely preventive. Treatment of PLHIV in Nigeria commenced in 2002 and has increased from about 15,000 to more than 1.78 million PLHIVs in 2023. Despite this progress in treatment coverage, however, KPs are not equitably covered. To address this gap, the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) launched an ambitious initiative-the Key Population Investment Fund (KPIF)-to target the unaddressed HIV-related needs of key populations (KPs) who are disproportionately affected by HIV. The KPIF initiative was implemented through partner organizations such as the Society for Family Health (SFH), a KP-friendly and indigenous non-governmental organization. Earlier, the program implemented by SFH was largely an HIV prevention program. SFH's transformation, transition, and growth to a comprehensive HIV prevention, care, and treatment service provider was necessary to bridge the gap in the needed expansion of HIV services to adequately meet the care needs of KPs and scale up programs. Therefore, this paper's aim is to share experiences in the transformation of SFH into a comprehensive HIV prevention, treatment, and care service provider in the hope that it may serve as a lesson for organizations with similar objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".