ArticleEarly intervention in psychiatry2025
Identifying Preliminary Risk Profiles for Dissociation in 16- to 25-Year-Olds Using Machine Learning.
Article in Early intervention in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Depersonalisation-derealisation as a transdiagnostic treatment target: a scoping review of the evidence in anxiety, depression, and psychosis.Frontiers in psychology · 2025Pooled it
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Dissociative Bipolar At-Risk Phenotype: Traumatic Burden and Subthreshold Affective Psychopathology in a Help-Seeking Youth Sample.Brain sciences · 2026Article
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Authors and funding
8 authors.
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Abstract
introductionDissociation 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.
methodsWe 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.
resultsMultiple regression indicated that four risk factors significantly contributed to FSA-dissociation, with relative order of contribution: everyday stress, childhood trauma, loneliness and marginalisation. Exploratory analysis using machine learning suggested dissociation results from a complex interplay between interpersonal, contextual, and intrapersonal pressures: alongside marginalisation and childhood trauma, negative self-concept and depression were important in younger (16-20 years), and anxiety and maladaptive emotion regulation in older (21-25 years) respondents.
conclusionsValidation of these findings could inform clinical assessment, and prevention and outreach efforts, improving the under-recognition of dissociation in mainstream services.
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