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Verfasst von:Dönnhoff, Ivo [VerfasserIn]   i
 Kindermann, David [VerfasserIn]   i
 Stahl-Toyota, Sophia [VerfasserIn]   i
 Nowak, Jonathan [VerfasserIn]   i
 Orth, Maximilian [VerfasserIn]   i
 Friederich, Hans-Christoph [VerfasserIn]   i
 Nikendei, Christoph [VerfasserIn]   i
Titel:Predictors for improvement in personality functioning during outpatient psychotherapy
Titelzusatz:a machine learning approach within a psychodynamic psychotherapy sample
Verf.angabe:I. Dönnhoff, D. Kindermann, S. Stahl-Toyota, J. Nowak, M. Orth, H.-C. Friederich, and C. Nikendei
E-Jahr:2024
Jahr:15 November 2024
Umfang:10 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 12.05.2025
Titel Quelle:Enthalten in: European psychiatry
Ort Quelle:Cambridge : Cambridge University Press, 1991
Jahr Quelle:2024
Band/Heft Quelle:67(2024), 1, Artikel-ID e79, Seite 1-10
ISSN Quelle:1778-3585
Abstract:BackgroundSince its introduction in the diagnostic manuals DSM-5 and ICD-11, the construct of personality functioning has gained increasing attention. However, it remains unclear which factors might predict improvement in personality functioning.MethodsWe examined a sample of 648 completed psychodynamic psychotherapies conducted by 172 therapists at the Heidelberg Institute for Psychotherapy. A machine learning approach was used to filter for variables that are relevant for the prediction of the improvement of personality functioning from a broad data set of variables collected at the beginning of each psychodynamic psychotherapy.ResultsOn average, we found an improvement of 0.24 (SD = 0.48) in the OPD-SQ. This corresponds to a medium effect in the improvement of personality functioning. Patients with initially high impairment experienced particularly large improvements. Overall, we found a large number of variables that proved to be predictive for the improvement of personality functioning. Limitations in social activity due to physical and emotional problems proved to be one of the most important predictors of improvement. Most of the effect sizes were small.ConclusionsOverall, the improvement in personality functioning during psychotherapy is determined more by the sum of a large number of small effects than by individual variables. In particular, variables that capture social areas of life proved to be robust predictors.
DOI:doi:10.1192/j.eurpsy.2024.1780
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1192/j.eurpsy.2024.1780
 kostenfrei: Volltext: https://www.cambridge.org/core/journals/european-psychiatry/article/predictors-for-improvement-in-personality-functionin ...
 DOI: https://doi.org/10.1192/j.eurpsy.2024.1780
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:machine learning
 missing data analysis in machine learning
 personality functioning
 psychotherapy success
K10plus-PPN:1925341984
Verknüpfungen:→ Zeitschrift

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