Endorsement of HIV-related stigma among men in Ghana: What are the determinants?
Bibliographic record
Abstract
INTRODUCTION: Stigma and discrimination against people living with HIV (PLHIV) remain a major barrier to effective HIV prevention. Despite the understanding that the creation of a socially inclusive environment for PLHIV is crucial for the promotion of testing, status disclosure, and treatment uptake, HIV stigma persists. Additionally, evidence suggests the endorsement of HIV stigma may be gender specific. Nonetheless, very little is known about the factors influencing men's discrimination against PLHIV in the Ghanaian context. Guided by the theory of planned behavior, our study fills this void by exploring the factors associated with the endorsement of HIV stigma in Ghana. METHODS: Utilizing a nationally representative data from the 2022 Ghana Demographic and Health Survey (DHS) (N = 7044 men with ages ranging from 15-49 years), and applying logistic regression models, this study examined the factors associated with the endorsement of HIV-related stigma in Ghana. RESULTS: The notion that HIV can be transmitted through the sharing of food with PLHIV was significantly associated with increased odds of stigma endorsement against children with HIV (OR = 3.381; P<0.001) and vendors with HIV (OR = 3.00; P<0.001). On the contrary, knowing that a healthy-looking person can have HIV was significantly associated with decreased odds of endorsement of stigma against children living with HIV (OR = 0.505; P<0.001), and vendors living with HIV (OR = 0.573; P<0.001). Likewise, having knowledge of drugs that help PLHIV to live longer, was significantly associated with decreased odds of stigma endorsement against children living with HIV (OR = 0.768; P<0.001), and vendors living with HIV (OR = 0.719; P<0.001). Moreover, participants with higher educational attainment reported lower odds of stigma endorsement against children living with HIV (OR = 0.255; P<0.01), and vendors living with HIV (OR = 0.327; P<0.01). Furthermore, age was significant and inversely associated with the endorsement of HIV stigma against children living with HIV (OR = 0.951; P<0.05), and vendors living with HIV (OR = 0.961; P<0.05). Also, wealth, ethnicity, and the region of residence significantly predicted endorsement of HIV stigma. CONCLUSION: For Ghana to achieve UNAIDS target 95-95-95 by 2030, targeted educational campaigns are necessary to dispel misconceptions about HIV and to promote social inclusion for reducing HIV-related stigma and discrimination in the country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".