The consequences of dismantling community-led HIV programming in Kenya: a call to action from the HEKA programme research partnership in Kenya
Bibliographic record
Abstract
In February 2025, the Health Research Intervention Kuthamini Afya Yetu (HEKA) programme research partnership held a 3-day workshop as part of a project aimed at developing a framework for community-based participatory mathematical modelling of HIV epidemics.1 The project is led by the Community Research and Technical Support Hub—an emerging research-focused hub aimed at uniting key population communities in research, learning exchanges and knowledge translation—and brought together a consortium of programme managers and monitoring and evaluation experts from seven community-based organisations run by and for gay, bisexual and other men who have sex with men (GBMSM) across Kenya, alongside academic researchers. However, on 24 January 2025, just 10 days prior to our meeting, organisations globally had received notices to ‘stop work’—interrupting decades of HIV prevention and treatment service delivery, supported through US funding.2 The announcement of the stop work orders had an immediate effect on GBMSM communities. We therefore catalysed part of our workshop to reflect on the impact of the orders on GBMSM community organisations and their members.
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 | 0.063 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.019 | 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".