Integrating exploration and prediction in computational psychotherapy science: proof of concept.

ABSTRACT:

Introduction

Psychotherapy research has long preferred explanatory over predictive models. As a result, psychotherapy research is currently limited in the variability that can be accounted for in the process and outcome of treatment. The present study is a proof-of-concept approach to psychotherapy science that uses a datadriven approach to achieve robust predictions of the process and outcome of treatment.

Methods

A trial including 65 therapeutic dyads was designed to enable an adequate level of variability in therapist characteristics, overcoming the common problem of restricted range. A mixed-model, data-driven approach with cross-validation machine learning algorithms was used to predict treatment outcome and alliance (within- and between-clients; client- and therapist-r

SUBMITTER: Fisher H 

PROVIDER: S-EPMC10811256 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature