Using machine learning algorithms to predict the effects of change processes in psychotherapy: Toward process-level treatment personalization.
Journal
Psychotherapy (Chicago, Ill.)
ISSN: 1939-1536
Titre abrégé: Psychotherapy (Chic)
Pays: United States
ID NLM: 2984829R
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
pubmed:
5
10
2023
medline:
5
10
2023
entrez:
5
10
2023
Statut:
ppublish
Résumé
This study aimed to develop and test algorithms to determine the individual relevance of two psychotherapeutic change processes (i.e., mastery and clarification) for outcome prediction. We measured process and outcome variables in a naturalistic outpatient sample treated with an integrative treatment for a variety of diagnoses (
Identifiants
pubmed: 37796546
pii: 2024-14837-001
doi: 10.1037/pst0000507
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
536-547Subventions
Organisme : Universität St.Gallen