ArticleEuropean addiction research2026
Risk Modeling and Association Pathways Integrating Psychosocial Factors for Drug Misuse in Adolescents and Young Adults: A Machine Learning Approach.
Article in European addiction research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
<p>Introduction: Early identification for preventing drug misuse among adolescents and young adults (AYAs) is more cost-effective than drug treatment. However, there is a lack of scientific and comprehensive risk models for early identification. This study aimed to construct risk models and association pathways that integrate psychosocial factors influencing drug misuse in AYAs using a machine learning approach.
methodsThis cross-sectional study included 1,012 Chinese AYAs aged 14-35 years and was conducted from June to December 2023. Key psychosocial factors for drug misuse were identified using the least absolute shrinkage and selection operator and Boruta. A voting classifier combining logistic regression, K-nearest neighbors, support vector machine, and 5-fold cross-validation was used for model training and bootstrapping method wan applied for testing. Shapley additive explanation was used to determine the importance of each risk factor. A Bayesian network (BN) model was constructed to explore possible association pathways for drug misuse.
resultsA total of 3.85% of participants reported drug misuse. Significant risk factors included environmental exposure to drug misusers, working in nightlife venues (WNVs), cognition of traditional drug types, perceived risks of drug misuse, permissive attitude toward drug misuse, refusal self-efficacy of drug misuse (RSEDM), externalizing problem behavior, sensation-seeking, and adverse childhood experiences (ACEs). The voting model showed strong performance (recall: 94.10%; area under the curve: 98.11%; accuracy: 92.63%; precision: 34.15%; F-score: 49.68%). The BN model revealed that ACEs played a central role in risk assessment of drug misuse. AYAs WNVs, with low RSEDM and exposure to drug misusers, had exceeding 90% probability of drug misuse.
conclusionACEs serve as a primary risk factor, with a significant impact on social environmental influences. AYAs with ACEs and those WNVs require targeted and early interventions to prevent drug misuse. </p>.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.