Identifying Preliminary Risk Profiles for Dissociation in 16- to 25-Year-Olds Using Machine Learning.

ABSTRACT:

Introduction

Dissociation is associated with clinical severity, increased risk of suicide and self-harm, and disproportionately affects adolescents and young adults. Whilst evidence indicates multiple factors contribute to dissociative experiences, a multi-factorial explanation of increased risk for dissociation has yet to be achieved.

Methods

We used multiple regression to investigate the relative influence of five plausible risk factors (childhood trauma, loneliness, marginalisation, socio-economic status, and everyday stress), and machine learning to generate tentative high-risk profiles for 'felt sense of anomaly' dissociation (FSA-dissociation) using cross-sectional online survey data from 2384 UK-based 16- to 25-year-olds.

Results

Multiple regression indicated t

SUBMITTER: McGuinness R 

PROVIDER: S-EPMC11808442 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature