EU-SPR Online Debate: Is machine learning up to the challenge of capturing the complexity of psychotherapy?

    Activity: Participating in or organising an event typesParticipation in workshop, seminar, course


    The European Chapter of the Society for Psychotherapy Research (EU-SPR) is proud to host an online debate on the relevance, the problems and the potential of machine learning in psychotherapy research.

    Ueli Kramer, President EU-SPR (Switzerland) and Jan Boehnke, host of the 2021 EU-SPR online Dundee workshops for young researchers (UK)

    Katie Aafjes-van Doorn, Yeshiva University (USA)
    Kellyn Arnold, University of Leeds (UK)
    Franz Caspar, University of Bern (Switzerland)
    Sigal Zilcha-Mano, University of Haifa (Israel)

    In 2020, the term "machine learning" is used to describe current data analytic approaches, which particularly in health care research are used to identify target populations and allocate treatments: this process may support the "personalisation" of psychotherapy. The term of machine learning is fuzzy around its edges and is used to describe a diverse array of established statistical approaches, epistemic practices, as well as new developments in quantitative methods. The goal of the discussion is to look behind the current hype around these approaches and explore the meaning and potential in the context of psychotherapy research.

    Among others, these are the questions up for debate:

    What are the key quality criteria for successful application of machine learning?
    Will the 2030 psychotherapy researcher rely on machine learning only to draw conclusions?
    For which type of research questions may machine learning be most appropriate, for which research questions less so?
    Which new research questions, or areas of study, may be addressed using machine learning?
    How will conclusions from machine learning research be useful to inform the practice of psychotherapy?
    Period11 Dec 2020
    Event typeSeminar