See Events

FULLY DIGITAL - NO PUBLIC : PhD Defence Wouter Smink | What works when for whom? - A methodological reflection on therapeutic change process research

What works when for whom? - A methodological reflection on therapeutic change process research

Due to the COVID-19 crisis measures the PhD defence of Wouter Smink will take place online.

The PhD defence can be followed by a live stream.

Wouter Smink is a PhD student in the research group Psychology, Health & Tecnology. His supervisors are prof.dr. G.J. Westerhof and B.P. Veldkamp from the Faculty of Behavioural, Management and Social Sciences (BMS).

We aim to advance Therapeutic Change Process Research (TCPR), a field dedicated to find out what treatment –by whom and under which set of circumstances– is most effective for this individual with that specific problem. Our approach advocates that assessing the therapeutic exchange between client and counsellor provides a possibility to open the ‘black box’ of therapy to learn more about What Works When for Whom (WWWW). Web-based interventions provide an unique opportunity for TCPR: as online counselling is effective, all active ingredients of therapy should be included in the exchanged e-mails. Through seven propositions, we argue why the e-mail based ‘talking cure’ contains a wealth of information about the WWWW question, and present an approach that consists out of three parts. In the first part of the thesis, we discuss the automated and qualitative TCPR methods that are used to study language. In the second, we discuss the TCPR models that are (and should be) used to model the results of these methods. We reflect on the differences between the models and methods through the automation-explication framework. We favour multilevel modelling methods for TCPR, but these models have a shortcoming: they cannot assess negative clustering effects. In the last part, we present a gentle introduction to Bayesian Covariance Structure Modelling: an alternative TCPR model that is capable of addressing the WWWW question by modelling negative clustering effects.