A decade and beyond: learnings from HIV programming with underserved and marginalized key populations in Kenya
Bibliographic record
Abstract
INTRODUCTION: Key populations (KP) continue to account for high HIV incidence globally. Still, prioritization of KP in the national HIV prevention response remains insufficient, leading to their suboptimal access to HIV programmes. This commentary aims to share Kenya's challenges and successes in achieving 2020 global HIV targets and scaling up the KP programme in the last decade. DISCUSSION: The KP programme in Kenya has scaled up in the last decade with the inclusion of female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), transgender people and people in prisons as priority populations in the national HIV response. KP coverage based on official size estimates for FSW is 73%, for MSM is 82%, for PWID through needle syringe programme (NSP) is 71%, and through opioid substitution therapy (OST) is 26% and for transgender people is 5%. The service outcomes for KP have been relatively strong in prevention with high condom use at last paid sex for FSW (92%) and use of sterile equipment among PWID (88%), though condom use at last sex with a non-regular partner among MSM (78%) is still low. The KP programme has not met care continuum targets for all subpopulations with low case findings. The national KP programme led by the Ministry of Health has scaled up the programme through (a) strategic partnerships with KP-led and competent organizations, researchers and donors; (b) development of policy guidance and programme standards; (c) continuous sensitization and advocacy to garner support; (d) development of national reporting systems, among others. However, the programme is still struggling with uncertain size estimates; lack of updated bio-behavioural survey data; inadequate scale-up of interventions among transgender people and people in prison settings; gaps in reaching adolescent and young KP, and effectively addressing structural barriers like violence and stigma. CONCLUSIONS: To reach the ambitious global HIV targets, sufficient coverage of KP with quality HIV programmes is critical. Despite scaling up the KP programme, Kenya has not yet achieved the 2020 global HIV targets and needs more efforts to scale-up quality programmes for KP who are underserved in the HIV response.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".