An examination of the combined use of the PAI and the M-FAST in detecting malingering among inmates
Gaines, Michelle V.
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Severe mental illness is at least twice as common in correctional settings as in the general population (James & Glaze, 2006); however, mental health services resources remain limited in correctional settings (James & Glaze, 2006; Teplin, 1990). Complicating the issue of limited mental health resources, offenders in jails and prisons commonly feign symptoms of mental illness in order to receive treatment-related privileges (Rogers & Vitacco, 2002). Feigning inmates use mental health resources that would otherwise be allocated to genuinely mentally ill inmates, creating a need for clinicians to find efficient means of detecting feigning. The Structured Interview of Reported Symptoms (SIRS; Rogers, Bagby, & Dickens,1992), is currently the most widely validated feigning detection instrument. However, clinician time required to administer and score the SIRS renders the instrument inefficient in settings with limited mental health resources. The current study examined the use of the Miller Forensic Assessment of Symptoms Test (M-FAST; Miller, 2001) and the Personality Assessment Inventory (PAI; Morey, 1991) for increasing the efficiency of feigning detection. Archival data were examined from a male maximum-security correctional psychiatric inpatient sample (N = 100). Logistic regression analyses were performed using a combination of the M-FAST and PAI to distinguish inmates who feigned on the SIRS from non-feigning inmates. The variance in SIRS performance accounted for by the combined M-FAST/PAI model was large (93.6%) with high rates (94.7%) of classification accuracy. In addition, the M-FAST (92%) and the PAI (94.9%) individually performed well at classifying feigners and non-feigners. Consequently, logistic regression equations are presented for use as a clinical tool for calculating the probability that an individual would be classified as feigning on the SIRS, based on M-FAST and PAI scale scores.