Determinants and Distribution of Comprehensive HIV/AIDS Knowledge in Ghana
Bibliographic record
Abstract
Globally, nearly 37 million people are living with HIV with about 70 percent of these living in Sub-Saharan Africa (SSA). Stigma and discrimination remain one of the major barriers to preventing new infections in the country. However, misconceptions about HIV/AIDS have been indicated as one of the key drivers of the disease. Efforts to prevent new infections have not been entirely successful. Therefore, this study aims to examine the trends and distribution in comprehensive knowledge of HIV and AIDS and determine the factors associated with comprehensive awareness of HIV and AIDS among adult women and men. The study relies on data from three rounds of Ghana Demographic & Health Surveys conducted in 2003, 2008 and 2014 to show trends. Logistic regression was used for multivariate analysis. The thematic mapping of HI/AIDS comprehensive knowledge was conducted using ArcGIS version 10.4 using GPS coordinates in the 2014 GDHS which contained aggregated individual characteristics and HIV knowledge scores.While comprehensive HIV and AIDS knowledge is above 50% among adult population in Ghana, the results show a significant decrease in comprehensive knowledge from 72% in 2008 to 59% in 2013. The strongest predictors for having comprehensive knowledge were found to be education; gender, marital status, locality, occupation and wealth status. The paper demonstrates that preventive activities leading to improvement in the comprehensive knowledge of HIV and AIDS in Ghana is needed. Sound knowledge about HIV and AIDS is critical for the adoption of behaviours that reduce the risk of HIV transmission. Education on HIV prevention must be expanded to improve the comprehensive knowledge 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".