Examining HIV-related stigma and discrimination in Ghana: what are the major contributors?
Bibliographic record
Abstract
BACKGROUND: Although AIDS-related stigma and discrimination are considered detrimental to HIV prevention activities, not many studies have attempted to understand stigma and discrimination in sub-Saharan Africa, particularly Ghana. METHODS: Using the 2008 Ghana Demographic and Health Survey and applying the ordinary least-squares technique, this study examined what influences AIDS-related stigma and discrimination among men and women in Ghana. RESULTS: The results indicate that Ghanaian men and women with relatively high knowledge about HIV/AIDS had low stigmatising and discriminatory attitudes (b=-0.097, P<0.01; b=-0.083, P<0.01), respectively. On the other hand, respondents who endorsed more myths about HIV transmission had high stigma and discriminatory attitudes. Women who had ever tested for their HIV serostatus reported significantly lower levels of stigma and discrimination (b=-0.085, P<0.01) compared with those who had not tested for HIV. Individuals who are highly educated, employed and in wealthy households all reported significantly lower levels of stigma and discrimination compared with those who are uneducated, unemployed and in poorer households. CONCLUSION: AIDS-related stigma and discrimination can be reduced by encouraging HIV testing, and ensuring that Ghanaians understand and have factual knowledge regarding the transmission of the disease.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".