Kristin Jankowsky, Johannes Zimmermann, Ulrich Jaeger, Robert Mestel, Ulrich Schroeders
{"title":"第一印象很重要:治疗师对患者动机和帮助联盟的印象可预测心理治疗的放弃。","authors":"Kristin Jankowsky, Johannes Zimmermann, Ulrich Jaeger, Robert Mestel, Ulrich Schroeders","doi":"10.1080/10503307.2024.2411985","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed.</p><p><strong>Method: </strong>We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (<i>N</i> = 1,691 and <i>N</i> = 12,473) using baseline and initial process variables reported by patients and therapists.</p><p><strong>Results: </strong>Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients' motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout.</p><p><strong>Conclusions: </strong>Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists' first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients' motivation and the therapeutic alliance.</p>","PeriodicalId":48159,"journal":{"name":"Psychotherapy Research","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"First impressions count: Therapists' impression on patients' motivation and helping alliance predicts psychotherapy dropout.\",\"authors\":\"Kristin Jankowsky, Johannes Zimmermann, Ulrich Jaeger, Robert Mestel, Ulrich Schroeders\",\"doi\":\"10.1080/10503307.2024.2411985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed.</p><p><strong>Method: </strong>We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (<i>N</i> = 1,691 and <i>N</i> = 12,473) using baseline and initial process variables reported by patients and therapists.</p><p><strong>Results: </strong>Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients' motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout.</p><p><strong>Conclusions: </strong>Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists' first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients' motivation and the therapeutic alliance.</p>\",\"PeriodicalId\":48159,\"journal\":{\"name\":\"Psychotherapy Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psychotherapy Research\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1080/10503307.2024.2411985\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychotherapy Research","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/10503307.2024.2411985","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
First impressions count: Therapists' impression on patients' motivation and helping alliance predicts psychotherapy dropout.
Objective: With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed.
Method: We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (N = 1,691 and N = 12,473) using baseline and initial process variables reported by patients and therapists.
Results: Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients' motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout.
Conclusions: Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists' first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients' motivation and the therapeutic alliance.
期刊介绍:
Psychotherapy Research seeks to enhance the development, scientific quality, and social relevance of psychotherapy research and to foster the use of research findings in practice, education, and policy formulation. The Journal publishes reports of original research on all aspects of psychotherapy, including its outcomes, its processes, education of practitioners, and delivery of services. It also publishes methodological, theoretical, and review articles of direct relevance to psychotherapy research. The Journal is addressed to an international, interdisciplinary audience and welcomes submissions dealing with diverse theoretical orientations, treatment modalities.