Perception and experience of HIV-induced stigma among people with HIV seeking healthcare in Ghana
Bibliographic record
Abstract
BACKGROUND: Advances in health and technology have reduced HIV to a more manageable communicable disease. Yet, stigma and discrimination against people with HIV remain critical barriers to ending the pandemic by 2030. Due to limited literature on stigma and discrimination in Ghana, we aimed to assess the experiences and predictors of stigma among PWH seeking healthcare in selected health facilities. METHODS: This convergent parallel mixed-methods study involved 420 people with HIV responding to a quantitative survey and 25 PWH participating in qualitative interviews (9 in-depth interviews and 16 in focus group discussions). Respondents were recruited through systematic and purposive sampling techniques for the quantitative and qualitative aspects, respectively. Quantitative data were analyzed using Stata/SE version 16.0, with logistic regression models fitted to measure associations between predictor variables and experienced stigma. Qualitative data were analyzed thematically using NVivo software, employing an inductive approach. RESULTS: Of the 420 participants, 58 (13.8%) reported ever experiencing stigma due to their HIV status. Among those who experienced stigma, 44 (75.9%) reported stigma in their communities, 24 (41.4%) in their homes, 15 (25.9%) at their workplaces, and 13 (22.4%) at health facilities. The most common forms of stigma were being gossiped about (26.0%), verbal insults/harassment (15.2%), and physical assault (8.3%). Qualitative findings corroborated these experiences, revealing impacts on healthcare access, social relationships, and mental health. Females (aOR = 13.10, 95% CI: 1.64-104.55) and persons with TB-HIV co-infection (aOR = 20.53, 95% CI: 3.28-128.56) had greater odds of experiencing stigma. PWH who were self-employed had lower odds of experiencing stigma at the HIV clinic (aOR = 0.07, 95% CI: 0.01-0.53, p = 0.009). CONCLUSION: Experienced stigma ranged from low to moderate in different settings, with communities being the most common location. We observed differences in stigma experienced among PWH based on gender, employment status, and TB co-infection. These findings suggest a need for targeted, context-specific interventions to reduce HIV-related stigma in Ghana, with a particular focus on community-level interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".