Assessing Feigning With the Feigning Evaluation INtegrating Sources (FEINS) in a Forensic Psychiatric Sample

Evaluators of examinees in forensic contexts must consider the potential for falsified or exaggerated psychiatric symptoms and/or cognitive deficits. A number of validated assessment tools assist evaluators in identifying those examinees who feign impairment; however, no comprehensive method has been established for consolidating data from multiple tests, interviews, behavioral observations, and collateral sources. The current pilot study preliminarily examined the interrater reliability and validity of a new forensic assessment tool, the Feigning Evaluation INtegrating Sources (FEINS), developed to guide evaluators in the comprehensive assessment of feigning by adding structure to the collection of relevant data. Fifty-eight male pretrial defendants undergoing restoration of competency to stand trial at a state forensic psychiatric center participated in the study. Results provided preliminary support for reliability in scoring the FEINS, construct validity, and predictive validity. FEINS items that assessed clinical presentation, and those that guided the use of test data, were more useful than items capturing historical/demographic data. Structured professional judgments developed using the FEINS appeared to be more accurate in predicting competency evaluators' perceptions of feigning than both unstructured clinical judgment (i.e., referring psychologist's perception of feigning) alone and test data alone, using hierarchical multiple regressions. Findings suggest that the FEINS may have practical utility in guiding clinical opinions regarding feigning across psychiatric, cognitive, and psycholegal/functional domains. Impact Statement The present study explores the utility of the Feigning Evaluation INtegrating Sources (FEINS), a forensic assessment instrument designed to prompt clinicians to consider specific relevant factors when assessing the potential for feigning. Results suggest that the instrument, which utilizes a structured professional judgment approach, may be helpful to clinicians in their integration and consolidation of complex data from multiple sources.