Toward improving anal health care among GBMSM in Kenya: Lessons from a patient’s illness, treatment and recovery journey
Bibliographic record
Abstract
We aimed to illuminate the challenges of treating anal diseases among gay, bisexual and other men who have sex with men (GBMSM) in a homophobic climate amid resource constraints. Building on a long-term Kenyan-Canadian collaboration, an anal healthcare intervention study, known as the WEMAH Project, was initiated in January 2022. The initial collaborative work strengthened existing anal health services provided at a community-based clinic serving GBMSM in Nairobi. The illness and treatment journey for one patient is highlighted. Prior to attending the community clinical programme, reluctance to disclose symptoms to other healthcare workers, misdiagnoses and incorrect treatment, absence of physical exam conducted, lack of access to effective medications, and self-treatment attempts not only led to late disease detection but also exacerbated the illness itself, culminating in advanced disease progression accompanied by severe pain and suffering. However, once connected to the community clinic with the enhanced anal healthcare programme, the disease was effectively managed by the clinical team. Although specialised clinics have been established throughout Kenya to meet the STI-related needs of GBMSM, the constellation of factors highlighted in one patient's journey, from illness to recovery, reveals important gaps in Kenya's existing sexual health programme for GBMSM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".