Title: Self Perceptions of Risk for Patients with Co-Occurring Disorders
Dates: 5/1/2011 - 12/31/2014
Funder: National Institute of Mental Health
PI: Jennifer Skeem, Ph.D.
Abstract: This study seeks to determine the efficacy of self predictions of violence among people with co-occurring substance abuse disorders. People with co-occurring mental and substance abuse disorders appear to be at double the risk of violence. This study adapts a Conditional Model of Predication (CMP) to enhance understanding of clients' risk state and how it can be monitored in the outpatient context. The adapted CMP shifts focus from clinical predication to clients' self prediction. According to the CMP, clients have experienced-based schemas that specify the kind of violence they might become involved in, given particular conditions (e.g., drinking). We posit that clients' knowledge of their own "if…then" patterns equips them to assess their own risk state, and that clients' accuracy may be superior to current instruments for predicting violence. Our primary aims are to (1) compare the accuracy of patients' self predictions of violence risk with that of clinical judgment and two clinically feasible actuarial tools; (2) assess whether the accuracy of patients' self predictions of violence risk is increased with "cognitive scaffolding" (i.e., a clinical interview about past violence-relevant experiences); (3) explore whether patients' accuracy is based on the understanding of their own, risk-relevant "if…then" patterns; and, (4) determine whether patients make lower self-predicted violence risk assessments to clinicians than researchers. Policy makers and citizens are concerned about the adequacy of violence risk assessment and treatment services for high risk clients. If the proposed research supports the novel hypothesis that self prediction accurately captures violence risk state, its potential to assist clinicians in enhancing public safety is unparalleled.
Personnel: Charles Lidz, Ph.D.(Co-I)