"What if I get sick, where shall I go?": a qualitative investigation of healthcare engagement among young gay and bisexual men in Nairobi, Kenya
Bibliographic record
Abstract
Globally, young gay, bisexual and other men who have sex with men (YMSM) experience a disproportionate burden of disease compared to young heterosexual men and older MSM. However, YMSM experience major inequities in access and use of health services. We sought to gain a detailed understanding of YMSM's healthcare engagement experiences across public, private, tertiary institution-based and MSM-friendly health facilities in Nairobi, Kenya, to inform development of interventions to improve access and use of health services by YMSM.In September 2021, in-person qualitative in-depths interviews were conducted among 22 YMSM purposively sampled from 248 YMSM who had previously participated in a respondent-driven sampling integrated bio-behavioral survey. Interviews were done in English, transcribed verbatim and analyzed descriptively using NVivo version 12.Participants were 18-24 years old, all identified as cisgender male, three-quarters as gay and a quarter as bisexual. Themes that were defined from the analysis included: YMSM's experiences during healthcare seeking in various clinical settings, priority health needs, desired healthcare provider (HCP) characteristics, and the potential role of digital health interventions in improving access and use of health services. Participants relayed experiences of prejudice, stigma and discrimination when seeking services in public and institution-based health facilities, unlike in community pharmacies, private and MSM-friendly health facilities where they felt they were handled with dignity. Health needs prioritized by YMSM centered on prevention and control of HIV, sexually transmitted infections (STIs), depression and substance abuse. Participants desired HCPs who were empathetic, non-judgmental and knowledgeable about their unique health needs such as management of anorectal STIs. Participants highlighted the usefulness of digital media in offering telehealth consultations and health education on subjects such as HIV/STIs prevention.During engagement with healthcare, YMSM experience various barriers that may cause them to postpone or avoid seeking care hence resulting in poor health outcomes. There is need to equip HCPs with knowledge, skills and cultural competencies to enable them offer equitable services to YMSM. Considerations should also be made for use of digital health interventions that may help YMSM circumvent some of the aforementioned barriers to service access and use.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".