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. 2022 Sep 28;19(19):12358.
doi: 10.3390/ijerph191912358.

Computational Methods in Psychotherapy: A Scoping Review

Valeria Cioffi  1 , Lucia Luciana Mosca  1 , Enrico Moretto  1 , Ottavio Ragozzino  1 , Roberta Stanzione  1 , Mario Bottone  2 , Nelson Mauro Maldonato  2 , Benedetta Muzii  3 , Raffaele Sperandeo  1
Affiliations

Computational Methods in Psychotherapy: A Scoping Review

Valeria Cioffi et al. Int J Environ Res Public Health. .

Abstract

Background: The study of complex systems, such as the psychotherapeutic encounter, transcends the mechanistic and reductionist methods for describing linear processes and needs suitable approaches to describe probabilistic and scarcely predictable phenomena.

Objective: The present study undertakes a scoping review of research on the computational methods in psychotherapy to gather new developments in this field and to better understand the phenomena occurring in psychotherapeutic interactions as well as in human interaction more generally.

Design: Online databases were used to identify papers published 2011-2022, from which we selected 18 publications from different resources, selected according to criteria established in advance and described in the text. A flow chart and a summary table of the articles consulted have been created.

Results: The majority of publications (44.4%) reported combined computational and experimental approaches, so we grouped the studies according to the types of computational methods used. All but one of the studies collected measured data. All the studies confirmed the usefulness of predictive and learning models in the study of complex variables such as those belonging to psychological, psychopathological and psychotherapeutic processes.

Conclusions: Research on computational methods will benefit from a careful selection of reference methods and standards. Therefore, this review represents an attempt to systematise the empirical literature on the applications of computational methods in psychotherapy research in order to offer clinicians an overview of the usefulness of these methods and the possibilities of their use in the various fields of application, highlighting their clinical implications, and ultimately attempting to identify potential opportunities for further research.

Keywords: complex systems; graph theory; machine learning; neural networks; patient–therapist relationship; psychopathology; psychotherapy.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Selection of sources of evidence flow chart (n°: means “numbers”).
See this image and copyright information in PMC

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