Is There an Association Between Empathic Speech and Change Talk in Motivational Interviewing Sessions?

Algorithms for Empathy: Using Machine Learning to Categorize Common Empathetic Traits Across Professional and Peer-Based Conversations

Introduction In this article, we describe our creation of a machine-learning model that uses a combination of rule-based and natural language processing (NLP) algorithms. We show how this "Empathy Algorithm" was developed and how its results compare to three datasets of professional counseling and peer-led conversations.  Methods These conversation datasets were rated by people with varying degrees of empathetic expertise (from counselors to student volunteers) and labeled as either low- or high-quality empathy. Our methodology involved running both these "low-empathy" and "high-empathy" conversations through our algorithm and then looking for a correlation between conversations labeled "high empathy" and an increased presence of six empathy skills flagged by our algorithm.  Results We found positive correlations between four of the six skills that our algorithm measures (i.e., four empathizing skills showed up the same or more in each of the "high-empathy" conversations within the three datasets). This suggests that certain empathizing skills are not only consistently present in effective conversations but also quantifiable enough to be measured by today's machine-learning models. Conclusion While limitations of language, binary classifications, and non-verbal cues remain as opportunities for further development, using algorithms to objectively assess empathic skills represents an important step to improve client outcomes and refine communication practices for today's healthcare professionals.

The evolution of motivational interviewing

This review traces the development of motivational interviewing (MI) from its happenstance beginnings and the first description published in this journal in 1983, to its continuing evolution as a method that is now in widespread practice in many professions, nations and languages. The efficacy of MI has been documented in hundreds of controlled clinical trials, and extensive process research sheds light on why and how it works. Developing proficiency in MI is facilitated by feedback and coaching based on observed practice after initial training. The author reflects on parallels between MI core processes and the characteristics found in 70 years of psychotherapy research to distinguish more effective therapists. This suggests that MI offers an evidence-based therapeutic style for delivering other treatments more effectively. The most common use of MI now is indeed in combination with other treatment methods such as cognitive behaviour therapies.

Examining the influence of active ingredients of motivational interviewing on client change talk

Motivational Interviewing (MI) is an evidenced-based intervention designed to help clients explore and resolve ambivalence around substance use. MI combines a humanistic tradition with behavioral components to facilitate client decisions concerning behavior change. As such, there is marked interest in the relationship between the two active ingredients of MI - the relational, or person-centered, components and the technical, or directional, behavioral components - on client in-session language. Yet, few studies have examined how these active ingredients operate in concert. Therefore, the current study evaluated the constellation of relational skills associated with client language, as well as the influence of technical skills on the relationship between provider relational skills and client change language. Specifically, we tested a latent construct of relational skill and its direct association with the proportion of client change talk. We then explored the mediating role of reflections of change and sustain talk (RefCT and RefST) on this relationship. The data for this secondary analysis are from Project ELICIT (N = 131), a randomized control trial evaluating the effects of MI training on client change language. We found support for a latent construct of relational skill (i.e., empathy, acceptance, collaboration, and autonomy/support). However, the relational skill construct did not predict client change language. There was support for an indirect effect, such that relational skills predicted RefCT and RefST, and RefCT and RefST predicted client change language. These results suggest that the synergistic implementation of the relational and technical components of MI is critical to facilitating a higher percentage of change talk.