Screening for adverse childhood experiences among young people using drugs in Vietnam: related factors and clinical implications
Bibliographic record
Abstract
BACKGROUND: Research evidence shows a strong association between adverse childhood experiences (ACEs) and later-life substance use. But little is known about the prevalence and impact of ACEs among young people using drugs (YPUD) in Vietnam. METHOD: A cross-sectional study using respondent-driven sampling and peer recruitment methods was conducted among YPUD aged 16-24 in three cities in Vietnam. Eligible participants were screened for ACEs using the ACE-IQ, tested for HIV and hepatitis C, and assessed for sociodemographic and behavioral characteristics. RESULTS: Data were collected on 553 individuals whose median age was 20: 79% were male, 18.3% women and 2.7% transgender. Methamphetamine use was reported by 75.8% of participants. 85.5% reported at least one ACE and 27.5% had four ACEs or more. An ACE score of 4 or higher was associated with female and transgender, lower educational level, methamphetamine use, buying sex, depression, psychotic symptoms and expressed need for mental health support. CONCLUSIONS: ACEs were found to be very common among YPUD in Vietnam. It is therefore strongly recommended that these young people should be provided with a comprehensive and secure assessment and care that includes not only essential harm reduction and addiction treatment needs but also addresses their mental health needs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".