scholarly journals What Differentiates Poor- and Good-Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research, Part Two: Network Analyses

2020 ◽  
Vol 11 ◽  
Author(s):  
Giulio de Felice ◽  
Alessandro Giuliani ◽  
Omar C. G. Gelo ◽  
Erhard Mergenthaler ◽  
Melissa M. De Smet ◽  
...  
Keyword(s):  
Statistical Mechanics ◽  
Psychotherapy Research ◽  
Network Analyses ◽  
Good Outcome

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

2019 ◽  
Author(s):  
Giulio De Felice ◽  
Franco Orsucci ◽  
Andrea Scozzari ◽  
Omar Gelo ◽  
Gabriele Serafini ◽  
...  
Keyword(s):  
Statistical Mechanics ◽  
Multivariate Time Series ◽  
Specific Effect ◽  
Psychotherapy Research ◽  
Principal Component ◽  
Coarse Grained ◽  
Emergent Properties ◽  
Good Outcome ◽  
Poor Outcome ◽  
Abstract Language

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 analysis (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 analysis, based on five coarse-grained descriptors related to variability, 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 to shift from a metaphorical to a fully quantitative status.

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

Systems ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 22 ◽  
Author(s):  
Giulio Felice ◽  
Franco Orsucci ◽  
Andrea Scozzari ◽  
Omar Gelo ◽  
Gabriele Serafini ◽  
...  
Keyword(s):  
Statistical Mechanics ◽  
Multivariate Time Series ◽  
Specific Effect ◽  
Psychotherapy Research ◽  
Principal Component ◽  
Coarse Grained ◽  
Emergent Properties ◽  
Good Outcome ◽  
Poor Outcome ◽  
Abstract Language

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.

scholarly journals Toward Data-Based Case Conceptualization: A Functional Analysis Approach With Ecological Momentary Assessment

2020 ◽  
Author(s):  
Saskia Scholten ◽  
Tanja Lischetzke ◽  
Julia Glombiewski
Keyword(s):  
Functional Analysis ◽  
Treatment Planning ◽  
Emotional Disorders ◽  
Ecological Momentary Assessment ◽  
Psychotherapy Research ◽  
Case Conceptualization ◽  
Network Analyses ◽  
The Individual ◽  
Ecological Momentary ◽  
Momentary Assessment

Network analyses and process-based approaches to psychotherapy are thrilling developments for psychotherapy research and practice, but they lack a therapeutic rationale for the individual selection of treatment modules. Conceptualizing the conditional relations around human responses using functional analysis could guide case conceptualization and treatment planning. In a pilot study with four participants (a 30- and a 25-year-old man; a 19- and a 44-year-old woman), we developed and tested the feasibility and acceptance of an assessment that comprises elements of functional analysis for participants with emotional disorders. We assessed an individualized set of items three times per day, for a period of 30 days, with ecological momentary assessment while participants were waiting for psychotherapy. The implementation proved to be both feasible and accepted; participants did not report any side effects. Three datasets were included in the analysis; one had to be excluded because the minimum response rate of 80% (out of 90 data points) was not met. P-factor and network analyses revealed meaningful behavioral clusters (e.g., participant 1: hopelessness, procrastination, coping, avoidance). The assessment is a promising diagnostic tool that helps participants and therapists identify and systemize relevant behavior patterns and to draw conclusions for treatment planning.

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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