"But the moment they find out that you are MSM…": a qualitative investigation of HIV prevention experiences among men who have sex with men (MSM) in Ghana's health care system
Bibliographic record
Abstract
The prevalence of HIV in Ghana is 1.3%, compared to 17% among men who have sex with men (MSM). There is limited empirical data on the current health care climate and its impact on HIV prevention services for Ghanaian MSM. The purposes of this study were to investigate (1) MSM's experiences using HIV prevention resources, (2) what factors, including health care climate factors, influenced MSM's use of prevention resources and (3) MSM self-identified strategies for improving HIV/sexually transmitted infection (STI) prevention among MSM in Ghanaian communities.We conducted 22 focus groups (n = 137) with peer social networks of MSM drawn from three geographic communities in Ghana (Accra, Kumasi, Manya Krobo). The data were examined using qualitative content analysis. Interviews with individual health care providers were also conducted to supplement the analysis of focus group findings to provide more nuanced illuminations of the experiences reported by MSM.There were four major findings related to MSM experiences using HIV prevention resources: (1) condom quality is low, condom access is poor, and condom use is disruptive, (2) inaccurate information undermines HIV testing (3), stigma undermines HIV testing, and (4) positive attitudes towards HIV prevention exist among MSM. The main healthcare climate factors that affected prevention were that MSM were not free to be themselves, MSM were not understood by healthcare providers, and that MSM did not feel that healthcare providers cared about them. To improve HIV prevention MSM suggested increased education tailored to MSM should be provided to enable self-advocacy and that education and awareness are needed to protect human rights of MSM in Ghana.MSM in Ghana are exposed to negative health care climates. Health care spaces that are unsupportive of MSM's autonomy undermine the uptake of prevention measures such as condoms, HIV testing, and accurate sexual health education. These findings contribute to knowledge to inform development of HIV prevention interventions for MSM in Ghana, such as culturally appropriate sexual health education, and digital technology to connect individuals with resources supportive of MSM.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".