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Clinical and Actuarial Predictions of Violence (From Modern Scientific Evidence: The Law and Science of Expert Testimony, V 1, P 300-318, 1997, D Faigman, D Kaye, M Saks, and J Sanders, eds.)

NCJ Number
167165
Author(s)
J Monahan
Date Published
1997
Length
19 pages
Annotation
This study reviews the relevant literature to determine whether there is a consensus among researchers on whether mental disorder is a risk factor for violence and whether there is validity to clinical predictions of violence.
Abstract
The data reviewed, which have only become available since 1990, suggest that mental disorder may be a statistically significant risk factor for violent behavior. By all indications, the great majority of those who are currently mentally disordered (approximately 90 percent) are not violent. Compared to the magnitude of risk associated with the combination of male gender, young age, and lower socioeconomic status, for example, the risk of violence presented by mental disorder is modest. Clearly, however, mental health status makes at best a trivial contribution to the overall level of violence in society. Regarding clinical and actuarial predictions of violence, recent studies suggest that clinical judgment has been undervalued in previous research. The study by Lidz et al., for example, concludes that clinicians were more reliable than chance in identifying a statistically more violent group, and the violence that the predicted group committed was more serious than the acts of the comparison group. The most recent critical analysis of existing risk assessment research advised that clinicians are able to distinguish violent from nonviolent patients with a modest, better-than-chance level of accuracy. Future research on the relationship between violence and mental disorder is likely to focus not on whether such a relationship exists, but on the precise form that the relationship takes. Research on the validity of predictions of violence among people with mental disorder is likely to emphasize reliance on actuarial approaches as the best hope to improve predictive accuracy. 64 footnotes

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