HIV testing amid COVID-19: community efforts to reach men who have sex with men in three Kenyan counties
Bibliographic record
Abstract
In comparison to European and American countries, Kenya has been less impacted by the COVID-19 pandemic in terms of reported cases and mortalities. However, everyday life has been dramatically affected by highly restrictive government-imposed measures such as stay-at-home curfews, prohibitions on mobility across national and county boundaries, and strict policing, especially of the urban poor, which has culminated in violence. This open letter highlights the effects of these measures on how three community-based organizations (CBOs) deliver HIV programs and services to highly stigmatized communities of men who have sex with men living in the counties of Kisumu, Kiambu and Mombasa. In particular, emphasis is placed on how HIV testing programs, which are supported by systematic peer outreach, are being disrupted at a time when global policymakers call for expanded HIV testing and treatment targets among key populations. While COVID 19 measures have greatly undermined local efforts to deliver health services to members and strengthen existing HIV testing programs, each of the three CBOs has taken innovative steps to adapt to the restrictions and to the COVID-19 pandemic itself. Although HIV testing in clinical spaces among those who were once regular and occasional program attendees dropped off noticeably in the early months of the COVID-19 lockdown, the program eventually began to rebound as outreach approaches shifted to virtual platforms and strategies. Importantly and unexpectedly, HIV self-testing kits proved to fill a major gap in clinic-based HIV testing at a time of crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".