[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123826-en":3,"doc-seo-123826-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123826,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Identifying risk profiles for dissociation in 16-to 25-year-olds using machine learning","Dissociation is linked to higher clinical severity, greater suicide and self-harm risk, and disproportionate impact on adolescents and young adults. Existing research suggests multiple contributing factors, yet a comprehensive multi-factor explanation for increased dissociation risk remains limited. This study recruited 2,384 UK participants aged 16–25 via an online cross-sectional survey to test five plausible risk factors and derive a tentative high-risk profile for felt sense of anomaly subtype dissociation using multiple regression and exploratory machine learning.","University of Birmingham  \nIdentifying risk profiles for dissociation in 16-to 25-year-olds using machine learning  \nMcGuinness, Roberta; Herring, Daniel; Wu, Xinyi; Almandi, Maryam; Bhangu, Daveena; Collinson, Lucia; Shang, Xiaocheng; Černis, Emma  \nDOI:  \n[10.31234/osf.io/j54v3](10.31234/osf.io/j54v3)  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nOther version  \nCitation for published version (Harvard):  \nMcGuinness, R, Herring, D, Wu, X, Almandi, M, Bhangu, D, Collinson, L, Shang, X & Černis, E 2023 ' Identifying risk profiles for dissociation in 16-to 25-year-olds using machine learning' PsyArXiv.  \n[https://doi.org/10.31234/osf.io/j54v3](https://doi.org/10.31234/osf.io/j54v3)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless 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.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•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.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile 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.  \nIf 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.  \nDownload date: 03. Aug. 2026  \nIdentifying risk profiles for dissociation in 16-to 25-year-olds using machine learning.  \nRoberta McGuinness 1 , Daniel Herring 2 , Xinyi Wu 2 , Maryam Almandi 1 , Daveena Bhangu 1 , Lucia Collinson 1 , Xiaocheng Shang 2,3 , Emma Černis *1,4  \n1. School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK  \n2. School of Mathematics, University of Birmingham, Birmingham, B15 2TT, UK  \n3. The Alan Turing Institute, British Library, London, NW1 2DB, UK  \n4. Institute for Mental Health, University of Birmingham, Birmingham, B15 2TT, UK  \n* Corresponding author: Dr Emma Černis  [e.cernis@bham.ac.uk](e.cernis@bham.ac.uk)  \nDeclaration of Interest  \nDeclaration of interest: None.  \nFunding  \nThis work was funded by University of Birmingham internal funding awarded by the School of Psychology, Institute for Global Innovation, and Institute for Interdisciplinary Data Science and AI. XS acknowledges the support of the London Mathematical Society through the Emmy Noether Fellowship (reference number EN-2223-09) . The funding sources had no involvement in the study design; in the collection, analysis and interpretation of data; in the writing of the report; nor in the decision to submit the article for publication.  \nData Availability  \nThe data that support the findings of this study are openly available via the Open Science Framework (OSF) at [http://doi.org/10.17605/OSF.IO/3XHU7](http://doi.org/10.17605/OSF.IO/3XHU7)  \nAuthor Contribution  \nRoberta McGuinness: data curation, investigation, writing – original draft preparation (lead), writing – review & editing. Daniel Herring: conceptualisation, data curation, formal analysis, software, visualisation, writing – original draft preparation, writing – review & editing","cbCaijlpDJpDFQYg","https://ap.wps.com/l/cbCaijlpDJpDFQYg","pdf",755916,1,23,"English","en",105,"# Abstract\n## Study aims and design\n## Risk factors and modeling approaches\n## Key findings","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To identify high-risk profiles for dissociation in people aged 16–25 by examining the relative influence of several proposed risk factors using regression and exploratory machine learning.\"},{\"question\":\"Which five risk factors were evaluated?\",\"answer\":\"Childhood trauma, loneliness, marginalisation, socio-economic status, and everyday stress.\"},{\"question\":\"Which factors were most predictive for felt sense of anomaly subtype dissociation?\",\"answer\":\"Multiple regression showed four significant contributors, in order: everyday stress, childhood trauma, loneliness, and marginalisation.\"}]","Identifying risk profiles for dissociation in 16-to 25-year-olds using machine learning | 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