Trends and determinants of HIV transmission among men who inject drugs in the Pokhara Valley, Nepal
Bibliographic record
Abstract
Abstract Background HIV is a major public health issue around the world, especially in developing countries. Although the overall prevalence of HIV in Nepal is relatively low and the country itself is considered low risk, there are specific sub-populations where the prevalence is far higher than the national average. One of these sub-groups is male injection drug users (IDUs). In order to understand the reasons for the differences in prevalence, a series of socio-demographic, behavioural and knowledge-based risk factors need to be assessed.Methods The study used a series of 7 cross-sectional survey datasets, collected between 2003 and 2017 (N=2,235) to investigate trends in HIV prevalence among male IDUs by key socio-demographic and behavioural and knowledge-based risk factors. A series of logistic regression models were conducted to investigate the association between study factors and HIV in the Pokhara valley, Nepal.Results A lower risk of HIV was associated with younger age (≤24 years compared to >24 years, OR = 0.17, 95% CI = 0.10, 0.31), not being married (OR = 0.51, 95% CI = 0.33, 0.80) and shorter duration of drug use (≤4 years compared to >4 years, OR = 0.16, 95% CI = 0.09, 0.29). A higher risk of HIV was associated with low (compared to secondary or higher) education level (OR = 2.76, 95% CI = 1.75, 4.36), a lack of addiction treatment (OR = 2.59, 95% CI = 1.64, 4.08), and recent use of unsterilized injection equipment (OR = 2.22, 95% CI = 1.20, 4.11).Conclusion Although knowledge of HIV is high among male IDUs, the prevalence of individuals who possess comprehensive knowledge and the number of individuals who have received treatment for drug addiction has decreased. This may indicate that addiction treatment and HIV education programs need to be strengthened.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".