Trends and determinants of HIV/AIDS knowledge among women in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Globally, women share an indiscriminate burden of the HIV epidemic and the associated socioeconomic consequences. Previous studies have demonstrated a positive correlation between levels of HIV knowledge with its prevalence. However, for Bangladesh such evidence is non-existent. In this study, we aimed to explore the extent of HIV knowledge in relation to the socio-demographic variables such as age, region, area of residence i.e., urban or rural, wealth index and education, and investigate the factors influencing the level of HIV knowledge among Bangladeshi women. METHODS: We used data from the Bangladesh Demographic and Health Survey (BDHS) survey conducted in 2011. In total 12,512 women ageing between 15 and 49 ever hearing about HIV regardless of HIV status were selected for this study. HIV knowledge level was estimated by analyzing respondents' answers to a set of 11 basic questions indicative of general awareness and mode of transmission. Descriptive statistics, cross-tabulation and multinominal logistic regression were performed for data analysis. RESULTS: Little over half the respondents had good knowledge regarding HIV transmission risks. The mean HIV knowledge score was -0.001 (SD 0.914). Average correct response rate about mode of transmission was higher than for general awareness. Educational level of women and sex of household head were found to be significantly associated with HIV knowledge in the high score group. Those with no education, primary education or secondary education were less likely to be in the high score group for HIV knowledge when compared with those with higher than secondary level of education. Similarly those with male as household head were less likely to be in the higher score group for HIV knowledge. CONCLUSIONS: Level of HIV knowledge among Bangladeshi women is quite low, and the limiting factors are rooted in various demographic and household characteristics. Education and sex of the household head have been found to be significantly correlated with the level of HIV knowledge and propound sound grounds for their incorporation in the future HIV prevention strategies. Education of women may also have wider ramifications allowing reduction in gender inequality, which in turn favors higher knowledge about HIV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".