STI/HIV Vulnerability among Out of School Youth in Vocational Training Centres in Vavuniya and Kilinochchi Districts
Bibliographic record
Abstract
Introduction: A decade after the civil war in Sri Lanka, the youth in the northern province had access to the rest of the world, with a gap of knowledge on sexual health. Objective: To determine the knowledge, attitude and high-risk behaviour towards HIV & STI and identify factors associated with high risk behaviours among out of school youth in vocational training centres in Vavuniya and Kilinochchi. Method: A cross-sectional study was conducted among 303 consenting out of school youth in vocational training centres were recruited in Vavuniya and Kilinochchi districts. Research validated scales were used to assess HIV knowledge and attitude. Bivariate statistical methods were used to understand the relationship between the variables. Results: Mean age was 18.5 years (SD=2.1 years), and 51% (n=155) were male. Only a quarter (n=76) had adequate HIV knowledge, and 11% (n=34) had the correct attitude. Sixty-three (21%) had consumed alcohol, and 34 (11%) had taken psychoactive drugs. Fifteen (5%) were sexually active, and 4 (1%) had received commercial sex. Adequate knowledge and attitude towards HIV were associated the sex (P=0.000, P=0.0045) and knowledge obtained from school (P=0.000, P=0.027). Conclusions: Even though risk behaviours for STI and HIV were low among out of school youth, the knowledge and attitude towards HIV/STI were poor.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".