Integrating group dynamics and cognitive-behavioral groups: A hybrid model

Using Clinicians' Ideal Social Climate Ratings in Group Therapy Training: Staff Development, Supervision, and Teaching

A group's norms are reflected in the group's social climate, and social climate dimensions have been associated with treatment outcomes in group therapy. However, novice group therapists are often not clear about which norms to develop in a given group. We describe a procedure in which clinicians specify their ideal social climate for a therapy group or a treatment setting on the Group Environment Scale (Moos, 2002) or the Community-Oriented Program Environment Scale (Moos, 1988) and compare their ideals with various reference groups and with one another. Discrepancies in ideal social climate ratings between co-therapists or among clinical staff can be explored in order to enhance coordinated interventions. This procedure has been used with experienced clinicians, trainees, and graduate students in group therapy courses.

ANALYSIS OF ROLLING GROUP THERAPY DATA USING CONDITIONALLY AUTOREGRESSIVE PRIORS

Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes.

Group dynamics and group psychotherapy revisited: four decades later

On the occasion of the 40th Anniversary of a symposium held on this controversial subject, the history of the relationship between group dynamics and group psychotherapy is reviewed. Within the contemporary framework of pluralism and integration, some guideposts are advanced that might help in the conceptual clarification of the affinity between these disparate, yet related, fields. This might lead, in turn, to a more refined elicitation of the specific therapeutic factors inherent in the group treatment modality.