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Record W4376126509 · doi:10.1093/pubmed/fdac163

Screening for adverse childhood experiences among young people using drugs in Vietnam: related factors and clinical implications

2023· article· en· W4376126509 on OpenAlexaff
Thanh Luan Pham, Thùy Linh Nguyễn, Kieu An Nguyen, John Paul Ekwaru, Olivier Phan, Laurent Michel, Thi Hai Oanh Khuat

Bibliographic record

VenueJournal of Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineEnvironmental healthEpidemiologyPublic healthAdverse Childhood ExperiencesAdverse effectFamily medicinePediatricsPsychiatryNursingMental healthPharmacologyPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.424
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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