Strengthening HIV preventive services for transgender women and men who have sex with men in coastal Kenya
Bibliographic record
Abstract
Men who have sex with men (MSM) and transgender women (TW), are at the highest risk for infection with the Human Immunodeficiency Virus (HIV). Globally, MSM and TW have higher prevalence of common mental disorders (CMD) including depression, anxiety and substance abuse. Finally, most of sub-Saharan Africa countries are rights constrained settings for both MSM and TW. The lack of legal recognition and protection for MSM and TW results in both internal and enacted stigma. Mental health challenges, stigma, lack of social protection and reduced access to healthcare services create a vicious cycle of increased risk for HIV infection. Pre-exposure prophylaxis (PrEP), could help prevent incident HIV infections in MSM and TW. In the empirical chapters of this thesis the author present studies in this thesis address the challenges MSM and TW face in accessing PrEP. The studies demonstrate the PrEP provision cascade including PrEP knowledge, desire to take it up, adherence and retention in follow-up. Additionally, the author explores the prevalence of CMD in MSM and any association with HIV status. In the discussion chapter, the author synthesises the findings from the studies against current literature. Finally, there are recommendations to improve HIV prevention for MSM and TW specifically in coastal Kenya. There are also recommendations addressing diagnosis and management of CMD in MSM. The author also gives suggestions for areas of further research especially in TW who are still underserved and underreached in sub-Saharan Africa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".