Modeling of forensic population current crime severity, based on past crime severity

David Nussbaum, Melanle MacEacheron, Melanle D. Douglass, Mark Watson, Stephanie L.S.B. Daoud, Gabrlela Me, Walter S. DeKeseredy

Research output: Contribution to journalReview articlepeer-review

Abstract

Predicting recidivistic severity in forensic populations would prove useful to tribunals deciding on sentence length, deciding on determinate versus indeterminate sentences, and applying "significant risk" statutes. In an exploratory study, we combine actuarial and self-report data to "predict" current severity of offending, in a forensic population in which all individuals are past offenders. Current criminal charges against a group of inmates (participants) in a Canadian, forensic psychiatric unit, were related to basic demographic and diagnosis information from psychiatric files, past offenses, and a few easily administered and scored pencil-and-paper tests. Many participants previously held Not Criminally Responsible due to Mental Disorder for at least one criminal offense. The collected information "predicted" current offense(s), producing R's of .60, .57 and .89 for N = 171 males and 28 females. Limitations include the need for replication with prospective designs and a better scale to measure severity of violence. Implications for practice and policy are discussed.

Original languageEnglish (US)
Pages (from-to)5-40
Number of pages36
JournalAmerican Journal of Forensic Psychology
Volume37
Issue number1
StatePublished - 2019
Externally publishedYes

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Applied Psychology

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