A multiplicative effect of Education and Wealth associated with HIV-related knowledge and attitudes among Ghanaian women
Bibliographic record
Abstract
BACKGROUND: Knowledge and attitudes regarding HIV play a crucial role in prevention and control efforts. Understanding the factors influencing HIV-related knowledge and attitudes is essential for formulating effective interventions and policies. This study aims to investigate the possibility of an interaction between education and wealth in influencing HIV-related knowledge and attitudes among women in Ghana. METHODS: Cross-sectional data from the Ghana Multiple Indicator Cluster Survey (MICS), a nationally representative sample, were analyzed. Statistical summaries were computed using place of residence, marital status, education level, wealth index quintile, use of insurance, functional difficulties, and exposure to modern media. Furthermore, a three-model Logistic regression analysis was conducted; Model 1 with main effects only, Model 2 with the interaction between education and wealth, and Model 3 with additional covariates. To account for the complexity of the survey data, the svyset command was executed in STATA. RESULTS: Although most interaction terms between wealth index quintiles and education levels did not show statistical significance, a few exceptions were observed. Notably, women with primary education in the second, middle, and fourth wealth quintiles, along with those with secondary education in the second wealth quintile, exhibited a negative significant association with HIV-related attitude level. However, no significant associations were found between other factors, including age, place of residence, marital status, and health insurance, and HIV-related attitude. The study also found significant associations between socioeconomic variables and HIV-related knowledge. There was a significant positive association between higher levels of education and HIV-related knowledge level. Women in wealthier quintiles had a significant positive association with HIV-related knowledge level. Factors such as place of residence and media exposure, including radio and television were also observed to be associated with HIV-related knowledge level. CONCLUSIONS: This study highlights the importance of socioeconomic status and media exposure in shaping HIV-related knowledge and attitudes among women in Ghana. Policy interventions should focus on reducing socioeconomic disparities, ensuring equitable access to education and healthcare services, and utilizing media platforms for effective HIV information dissemination.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".