Trends and predictors of knowledge about HIV/AIDS and its prevention and transmission methods among women in Tajikistan
Bibliographic record
Abstract
BACKGROUND: Prior research on HIV infections in Tajikistan and other Central Asian countries has focused primarily on injection drug users. Given the recent rise of heterosexual transmission, especially among women, there is a need to assess women's knowledge about HIV/AIDS and its methods of prevention and transmission across two time periods to examine cross-time changes and identify areas that need improvements. METHODS: Logistic regression and simulation of predicted probability analyses were based on data from Tajik women ranging in age from 15 to 49 who participated in the Multiple Indicator Cluster Survey (MICS) study in 2000 and 2005. RESULTS: We found that an over 2-fold increase in general knowledge about HIV/AIDS was accompanied by a substantial decrease in the ability to identify correct methods of prevention and to reject myths regarding its transmission. CONCLUSION: These alarming findings should prompt policy makers and program implementers to shift the focus of programs from raising general awareness to educating women about how HIV/AIDS is transmitted. Furthermore, rigorous efforts should be made to provide the most disadvantaged groups, including women of younger ages, with lower education, and from poor households with accurate information and adequate access to limited resources.
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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 | 0.001 | 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 | 0.001 |
| 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".