Ricerc@Sapienza

Aim: Although in the scientific literature there is a substantial evidence that psychodynamic psychotherapies produce significant symptomatic improvements, the dynamic of change occurring in the process of care remain mainly obscure. Hence, in this work we examine four good-outcome and four poor-outcome psychodynamic psychotherapies pertaining to one of the most renowned research studies: the York Depression Study. The main aim is to investigate the processes of change related to the use of language characterising good and poor outcome psychotherapies.
Method: Every segment of 150 words of brief psychodynamic psychotherapies (i.e. 18-20 sessions) was transcribed and coded. Each segment or statistical unit is clustered according to the K-means Algorithm and/or Minimum Spanning Tree, in order to show the temporal invariants between different segments. Now, each statistical unit will represent a specific configuration of the therapeutic field (i.e. the therapist-patient relationship) characterised by specific patterns of the above mentioned variables. Hence, each cluster will correspond to a specific relational configuration between patient and therapist, a specific state of the complex psychotherapeutic system. The cluster/states time serie generated will be studied by means of sophisticated mathematical analyses in order to identify the invariants within the good and poor outcome therapies processes. These statistical techniques capturing the non-linearity and non-stationarity of time series are, mainly: the Markov Transition Matrix, the Recurrence and the Cross-Recurrence Quantification Analysis, the Network Analyses.
Conclusions: this study for the first time in literature aim to analyse by means of statistical techniques that consider the non-linearity and non-stationarity of data the linguistic longitudinal dynamics of change, trying to understand their evolution characterising four poor and four good outcome brief psychodynamic psychotherapies.