Beyond biomedical and comorbidity approaches: Exploring associations between affinity group membership, health and health seeking behaviour among MSM/MSW in Nairobi, Kenya
Bibliographic record
Abstract
We explored general health and psychosocial characteristics among male sex workers and other men who have sex with men in Nairobi, Kenya. A total of 595 MSM/MSW were recruited into the study. We assessed group differences among those who self-reported HIV positive (SR-HIVP) and those who self-reported HIV negative (SR-HIVN) and by affinity group membership. Quality of life among SR-HIVP participants was significantly worse compared to SR-HIVN participants. Independent of HIV status and affinity group membership, participants reported high levels of hazardous alcohol use, harmful substance use, recent trauma and childhood abuse. The overall sample exhibited higher prevalence of moderate to severe depressive symptoms compared to the general population. Quality of life among participants who did not report affinity group membership (AGN) was significantly worse compared to participants who reported affinity group membership (AGP). AGN participants also reported significantly lower levels of social support. Membership in affinity groups was found to influence health seeking behaviour. Our findings suggest that we need to expand the mainstay biomedical and comorbidity focused research currently associated with MSM/MSW. Moreover, there are benefits to being part of MSM/MSW organisations and these organisations can potentially play a vital role in the health and well-being of MSM/MSW.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".