A Comparison of Comprehensive HIV/AIDS Knowledge Among Women Across Seven Post-Soviet Countries
Bibliographic record
Abstract
INTRODUCTION: Post-Soviet countries of Eastern Europe and Central Asia have witnessed a recent growth of HIV infection through heterosexual transmission. Women's low levels of knowledge about HIV prevention and transmission methods have been found to account for the higher female-to-male ratio among cases infected through the heterosexual route. This cross national comparison study assessed comprehensive HIV/AIDS knowledge and its key determinants among women of seven post-Soviet countries and identified which countries face the highest levels of risk due to the low levels of HIV/AIDS awareness. METHODS: Study data were obtained from the third wave of the Multiple Indicator Cluster Surveys (MICS3) (conducted in 2005 and 2006), nationally representative samples of women aged 15-49 years. Data on HIV/AIDS knowledge were analyzed for women in Kazakhstan (N=14,310), Kyrgyzstan (N=6,493), Tajikistan (N=4,676), Uzbekistan (N=13,376), Belarus (N=5,884), Ukraine (N=6,066), and Georgia (N=7,727) using descriptive statistics and ordinary least squares (OLS) regressions. RESULTS: We found that the percentage of women who could correctly identify all five modes of HIV/AIDS transmission and prevention was highest in Eastern European countries of Belarus (34.98%) and Ukraine (31.67%). Across all countries, the strongest predictors of comprehensive HIV/AIDS knowledge were age, education, and region of residence. Marital status, area of residence (urban vs. rural), and household wealth were significant predictors for several countries. CONCLUSION: High rates of comprehensive HIV/AIDS knowledge were found among women of Belarus and Ukraine. To reduce the spread of HIV in the region, programs promoting comprehensive HIV/AIDS knowledge for women of younger ages and with lower education are recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".