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An Examination of Predictive Validity and Change in Risk Factors for Stalking over Time
Stephanie R Penney1, Roy Ulrich2, Margaret Maheandiran2
1Dr. Penney is an Independent Scientist in the Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada, and an Assistant Professor in the Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Dr. Ulrich is a Staff Psychiatrist at the University of Alberta Hospital, Edmonton, Alberta, Canada. Ms. Maheandiran is a Research Coordinator in the Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. stephanie.penney@camh.ca.
Abstract:
This study investigates the predictive validity of two risk instruments for stalking, the Guidelines for Stalking Assessment and Management (SAM) and the Stalking Risk Profile (SRP), in a sample of 86 forensic psychiatric patients. We compare these tools against a well-validated violence risk assessment measure (Historical, Clinical, Risk Management-20, Version 3 (HCR-20V3)) for violent and stalking-related outcomes. Dynamic (mutable) components of each tool were rated at three annual intervals and revealed significant change across time. The HCR-20V3, SAM, and SRP measures showed comparable ability to classify those who recidivated with further stalking from those who did not (area under the curves = .72-.73, P < 001). Time-varying scores from the dynamic subscales of the HCR-20V3 and SAM contributed significantly to the prediction of stalking, whereas nonstalking violence was primarily forecast by the static (Historical) scale of the HCR-20V3. This suggests comparable validity of general violence and stalking risk tools for assessing the risk of stalking in forensic patients. Stalking-specific risk factors on the SAM and SRP will likely be of added clinical value in terms of tailoring risk management and treatment plans. Findings also emphasize the importance of attending to changes in risk status over time and incorporating time-sensitive methodologies into predictive models.
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