Extent of Knowledge about HIV and Its Determinants among Men in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Bangladesh is currently a low human immunodeficiency virus (HIV) prevalent country. However, the risk factors are widespread and the number of at-risk population is also rising, which warrants special policy attention. The risks of transmission were shown to be correlated with the level of HIV knowledge of individuals. In this study, we aimed to explore the level and influencing factors of HIV knowledge among adult men in Bangladesh. METHODOLOGY: Data for the present study were collected from the sixth round of Bangladesh Demographic and Health Survey. Participants were 3305 men between 15 and 54 years of age regardless of HIV status. The primary outcome variable was the HIV knowledge score, which was calculated by responses to questions regarding general concepts and the mode of transmission of HIV. Association between the HIV knowledge score and the explanatory variables were analyzed by binary logistic regression methods. RESULT: = 0.05; OR = 0.77, 95%CI = 0.60-10.00) were significantly associated with a high (equal or above mean value) HIV knowledge level. CONCLUSION: The level of HIV knowledge among Bangladeshi men is low. Leveraging HIV awareness programs targeting adult men to prevent future expansion of the epidemic should be a high priority. Revitalization and restructuring of the education sector and strengthening CHW's engagement to improve knowledge about HIV transmission among men could generate beneficial returns for HIV prevention programs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".