The role of the individual in the coming era of process-based therapy

For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy over the last few decades. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era.Accepted manuscrip

The application of sentiment analysis to a psychotherapy session : an exploratory study using four general-purpose lexicons

Dissertação de Mestrado apresentada no ISPA – Instituto Universitário para obtenção de grau de Mestre na especialidade de Psicologia Clínica.In this study we explore the application of sentiment analysis to a complete and in-person psychotherapy session. Sentiment analysis is a text mining technique that allows for the analysis, interpretation, and visualization of textual data. We investigate how we can apply a lexicon-based approach to analyze clinical session data, using four general-purpose lexicons available within an open-source statistical programming language environment, R. We conducted our study by comparing the performance of four general-purpose lexicons to the performance of n = 52 human raters, using inter-rater reliability (IRR) and intraclass correlation (ICC) measurements. Our findings suggest there is low to moderate agreement between human ratings and lexicon generated ratings, depending on the lexicon used. There are some benefits in applying a lexicon-based sentiment analysis approach to psychotherapy session data, namely the way it efficiently processes and analyses data and allows for novel visualizations of psychotherapy data. We recommend further investigation into the application of sentiment analysis as a technique, focusing on the performance of specific-purpose lexicons. We also recommend further research into comparing the performance of lexicon-based approaches to text classification approaches to the analysis of psychotherapy data

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models

Psychotherapy represents a broad class of medical interventions received by millions of patients each year. Unlike most medical treatments, its primary mechanisms are linguistic; i.e., the treatment relies directly on a conversation between a patient and provider. However, the evaluation of patient-provider conversation suffers from critical shortcomings, including intensive labor requirements, coder error, nonstandardized coding systems, and inability to scale up to larger data sets. To overcome these shortcomings, psychotherapy analysis needs a reliable and scalable method for summarizing the content of treatment encounters. We used a publicly available psychotherapy corpus from Alexander Street press comprising a large collection of transcripts of patient-provider conversations to compare coding performance for two machine learning methods. We used the labeled latent Dirichlet allocation (L-LDA) model to learn associations between text and codes, to predict codes in psychotherapy sessions, and to localize specific passages of within-session text representative of a session code. We compared the L-LDA model to a baseline lasso regression model using predictive accuracy and model generalizability (measured by calculating the area under the curve (AUC) from the receiver operating characteristic curve). The L-LDA model outperforms the lasso logistic regression model at predicting session-level codes with average AUC scores of 0.79, and 0.70, respectively. For fine-grained level coding, L-LDA and logistic regression are able to identify specific talk-turns representative of symptom codes. However, model performance for talk-turn identification is not yet as reliable as human coders. We conclude that the L-LDA model has the potential to be an objective, scalable method for accurate automated coding of psychotherapy sessions that perform better than comparable discriminative methods at session-level coding and can also predict fine-grained codes

Use of automated coding methods to assess motivational behaviour in education

Teachers’ motivational behaviour is related to important student outcomes. Assessing teachers’ motivational behaviour has been helpful to improve teaching quality and enhance student outcomes. However, researchers in educational psychology have relied on self-report or observer ratings. These methods face limitations on accurately and reliably assessing teachers’ motivational behaviour; thus restricting the pace and scale of conducting research. One potential method to overcome these restrictions is automated coding methods. These methods are capable of analysing behaviour at a large scale with less time and at low costs. In this thesis, I conducted three studies to examine the applications of an automated coding method to assess teacher motivational behaviours. First, I systematically reviewed the applications of automated coding methods used to analyse helping professionals’ interpersonal interactions using their verbal behaviour. The findings showed that automated coding methods were used in psychotherapy to predict the codes of a well-developed behavioural coding measure, in medical settings to predict conversation patterns or topics, and in education to predict simple concepts, such as the number of open/closed questions or class activity type (e.g., group work or teacher lecturing). In certain circumstances, these models achieved near human level performance. However, few studies adhered to best-practice machine learning guidelines. Second, I developed a dictionary of teachers’ motivational phrases and used it to automatically assess teachers’ motivating and de-motivating behaviours. Results showed that the dictionary ratings of teacher need support achieved a strong correlation with observer ratings of need support (rfull dictionary = .73). Third, I developed a classification of teachers’ motivational behaviour that would enable more advanced automated coding of teacher behaviours at each utterance level. In this study, I created a classification that includes 57 teacher motivating and de-motivating behaviours that are consistent with self-determination theory. Automatically assessing teachers’ motivational behaviour with automatic coding methods can provide accurate, fast pace, and large scale analysis of teacher motivational behaviour. This could allow for immediate feedback and also development of theoretical frameworks. The findings in this thesis can lead to the improvement of student motivation and other consequent student outcomes

A discourse pragmatic study to demystify empathic and empowering communicative processes in person-centred therapeutic interactions

A central tenet of person-centred therapy is that empathy and empowerment must be communicated in therapist-client interactions. Furthermore, empathy and empowerment are considered as related therapeutic processes. However, current theory about empathic and empowering communication lacks empirical evidence regarding how person-centred therapist-client dyads make these processes happen in practice. This thesis describes a linguistic study which adds knowledge about how empathic and empowering communications happen, and how they are related, in person-centred therapeutic interactions. A hybrid methodological framework comprising discourse analysis, conversation analysis and pragmatics approaches has been developed to address the complex and multifaceted nature of empathy and empowerment in person-centred therapeutic communications. Findings produced from the application of this framework are that empathy and empowerment are communicated in interactions by single, or combined, uses of reformulations, metaphors, personal pronouns, questions, and hedging. The overarching findings are suggestive that clients should be considered in agentic terms because they also actively contribute to the success of their therapy. Empathy and empowerment should also be understood, and researched, as being co-constructed processes. Further, views of power in person-centred therapeutic theory, especially how it relates to client empowerment, must regard its complexity and fluidity. The application of linguistic features for empathy and empowerment may also comprise a subtle strategy for therapists to address sensitive client issues and broach matters of blame and responsibility whilst simultaneously retaining the essential nondirective nature of person-centred therapeutic practice. Research suggestions include to expand the framework to incorporate alternate methodological approaches for analysing empathy and empowerment in related studies. Practice suggestions include that the findings be used to demystify empathic and empowering processes during person-centred therapeutic training. The findings may also be applied in support contexts which utilise person-centred therapeutic notions of empathy and empowerment, particularly when support is offered in textformat