Predicting working alliance in psychotherapy: A multi-modal machine learning approach.

Journal: Psychotherapy research : journal of the Society for Psychotherapy Research
PMID: 39743668

Abstract

OBJECTIVE: Session-by-session tracking of the working alliance enables clinicians to detect alliance deterioration and intervene accordingly, which has shown to improve treatment outcome, and reduce dropout. Despite this, regular use of alliance self-report measures has failed to gain widespread implementation. We aimed to develop an automated alliance prediction using behavioral features obtained from video-recorded therapy sessions.

Authors

  • Katie Aafjes-van Doorn
    Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA.
  • Marcelo Cicconet
    Image and Data Analysis Core, Harvard Medical School, Boston, MA, 02115, USA.
  • Jeffrey F Cohn
    University of Pittsburgh, Pittsburgh, PA, USA.
  • Marc Aafjes
    Deliberate.ai, New York, NY, USA.

Keywords

External Resources

Popular Topics
Recent Journals