  In order to increase mental health equity among the most vulnerable and
marginalized communities, it is important to increase access to high-quality
therapists. One facet of addressing these needs, is to provide timely feedback
to clinicians as they interact with their clients, in a way that is also
contextualized to specific clients and interactions they have had. Dynamical
systems provide a framework through which to analyze interactions. The present
work applies these methods to the domain of automated psychotherapist
evaluation for Cognitive Behavioral Therapy (CBT). Our methods extract local
dynamic modes from short windows of conversation and learns to correlate the
observed dynamics to CBT competence. The results demonstrate the value of this
paradigm and outlines the way in which these methods can be used to study and
improve therapeutic strategies.
