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What Differentiates Poor and Good Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research

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Department of Dynamic and Clinical Psychology, Sapienza University of Rome, 00185 Roma, Italy
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Department of Psychology, Niccolò Cusano Italian University in London, London E14 9TS, UK
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Department of Psychoanalysis, University College of London, London WC1E 6BT, UK
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Faculty of Economics, Niccolò Cusano University of Rome, 00166 Roma, Italy
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Department of History, Society and Human Studies, University of Salento, 73100 Lecce LE, Italy
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Faculty of Psychotherapy Science, Sigmund Freud University, A-1020, Vienna 1050, Austria
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Department of Economics, NC Italian University in London, London E14 9TS, UK
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Faculty of Psychology, Niccolò Cusano University of Rome, 00166 Roma, Italy
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Clinic of Psychosomatic Medicine and Psychotherapy, University of Ulm, 89081 Ulm, Germany
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Department of Environment and Health, Istituto Superiore di Sanità (Italian National Institute of Health) Roma, Viale Regina Elena 299, 00161 Roma, Italy
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Author to whom correspondence should be addressed.
Systems 2019, 7(2), 22; https://doi.org/10.3390/systems7020022
Received: 4 February 2019 / Revised: 3 April 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. “Static analyses” (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the “dynamic analyses”, based on five coarse-grained descriptors related to variability, the degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature enough to shift from a metaphorical to a fully quantitative status. View Full-Text
Keywords: psychotherapy; complex systems; statistical mechanics; process of change; nonlinear dynamics psychotherapy; complex systems; statistical mechanics; process of change; nonlinear dynamics
MDPI and ACS Style

de Felice, G.; Orsucci, F.F.; Scozzari, A.; Gelo, O.; Serafini, G.; Andreassi, S.; Vegni, N.; Paoloni, G.; Lagetto, G.; Mergenthaler, E.; Giuliani, A. What Differentiates Poor and Good Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research. Systems 2019, 7, 22.

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