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

Aug 4, 2026

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Dissociation is linked to clinical severity, elevated suicide and self-harm risk, and disproportionately affects adolescents and young adults. The work addresses the lack of an achieved multifactorial account of dissociative experiences by examining five plausible risk factors: childhood trauma, loneliness, marginalisation, socio-economic status, and everyday stress. Using multiple regression and machine learning on cross-sectional online survey data from 2384 UK participants, the study derives tentative high-risk profiles for felt sense of anomaly dissociation.

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Abstract

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University of Birmingham Identifying Preliminary Risk Profiles for Dissociation in 16-to 25-year-olds Using Machine Learning McGuinness, Roberta; Herring, Daniel; Wu, Xinyi; Almandi, Maryam; Bhangu, Daveena; Collinson, Lucia; Shang, Xiaocheng; Černis, Emma DOI: 10.1111/eip.70015 License: Creative Commons: Attribution-NonCommercial (CC BY-NC) Document Version Publisher's PDF, also known as Version of record Citation for published version (Harvard): McGuinness, R, Herring, D, Wu, X, Almandi, M, Bhangu, D, Collinson, L, Shang, X & Černis, E 2025, ' Identifying Preliminary Risk Profiles for Dissociation in 16-to 25-year-olds Using Machine Learning', Early Intervention in Psychiatry, vol. 19, no. 2, e70015 . [https://doi.org/10.1111/eip.70015](https://doi.org/10.1111/eip.70015) Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 03. Aug. 2026 Early Intervention in Psychiatry ORIGINAL ARTICLE OPEN ACCESS Identifying Preliminary Risk Profiles for Dissociation in 16-to 25-Year-Olds Using Machine Learning Roberta McGuinness1 | Daniel Herring2 | Xinyi Wu2 | Maryam Almandi1 | Daveena Bhangu1 | Lucia Collinson1 | Xiaocheng Shang2,3 | Emma Černis1,4 1School of Psychology, University of Birmingham, Birmingham, UK | 2School of Mathematics, University of Birmingham, Birmingham, UK | 3The Alan Turing Institute, British Library, London, UK | 4Institute for Mental Health, University of Birmingham, Birmingham, UK Correspondence: Emma Černis ([e.cernis@bham.ac.uk](e.cernis@bham.ac.uk)) Received: 3 September 2024 | Revised: 2 December 2024 | Accepted: 28 January 2025 Funding: This work was supported by University of Birmingham. Keywords: adolescent | dissociation | dissociative disorders | observational study | predictive modelling | psychopathology 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 multifactorial 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-base

FAQs

What is the primary goal of this study?

To identify preliminary high-risk profiles for dissociation in 16-to 25-year-olds by evaluating multiple risk factors and applying machine learning to generate tentative profiles.

Which risk factors were most influential in predicting felt sense of anomaly dissociation?

Everyday stress, childhood trauma, loneliness, and marginalisation significantly contributed, with everyday stress showing the highest relative order of contribution.

How did the machine learning findings differ across ages 16–20 versus 21–25?

For younger participants (16–20), negative self-concept and depression were important alongside marginalisation and childhood trauma; for older participants (21–25), anxiety and maladaptive emotion regulation played key roles.

Aug 4, 2026

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