Detecting defense mechanisms from Adult Attachment Interview (AAI) transcripts using machine learning.
Defensive functioning (i.e., unconscious process used to manage real or perceived threats) may play a role in the development of various psychopathologies. It is typically assessed via observer rating measures, however, human coding of defensive functioning is resource-intensive and time-consuming....
| Published in: | Psychotherapy Research Vol. 33; no. 6; pp. 757 - 768 |
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| Main Authors: | , , , , , |
| Format: | Article |
| Published: |
Taylor & Francis Ltd
Jul2023
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=164439877&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 164439877 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10503307 10T jtl: Psychotherapy Research issn: 10503307 maglogo: N pubinfo: dt: Jul2023 vid: 33 iid: 6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 164439877 10.1080/10503307.2022.2156306 ppf: 757 ppct: 11 formats: tig: atl: Detecting defense mechanisms from Adult Attachment Interview (AAI) transcripts using machine learning. aug: au: Tasca, Anthony N. Carlucci, Samantha Wiley, James C. Holden, Matthew El-Roby, Ahmed Tasca, Giorgio A. affil: School of Computer Science, Carleton University, Ottawa, Canada School of Psychology, University of Ottawa, Ottawa, Canada Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada Department of Psychology, Carleton University, Ottawa, Canada su: Binge-eating disorder Adults Communities Machine learning sug: subj: Binge-eating disorder Adults Communities Machine learning keyword: conversation analysis Defense Mechanism Rating Scale defensive functioning machine learning RoBERTa conversation analysis Defense Mechanism Rating Scale defensive functioning machine learning RoBERTa ab: Defensive functioning (i.e., unconscious process used to manage real or perceived threats) may play a role in the development of various psychopathologies. It is typically assessed via observer rating measures, however, human coding of defensive functioning is resource-intensive and time-consuming. The purpose of this study was to develop a machine learning approach to automate coding of defense mechanisms from interview transcripts. Participants included a clinical sample of women with binge-eating disorder (n = 92) and a community sample without binge-eating disorder (n = 66). We trained and evaluated five RoBERTa-based models to detect the presence of defenses in 16,785 interviewer-participant talk-turn pairs nested within 192 interviews. A model detected the presence of any defense, while four additional models detected the most common defenses in this sample (repression, intellectualization, reaction formation, undoing). The models were capable of distinguishing defenses (ROC-AUC.82-.90) but were not proficient enough to warrant replacing human coders (PR-AUC.28-.60). Follow-up analysis was performed to assess other practical uses of these models. Our machine learning models could be used to assist coders. Future research should conduct a deployment study to determine if human coding of defense mechanisms can be expedited using machine learning models. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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