Use of Dynamic Risk/Need Assessment Instruments Among Long-Term Incarcerated Offenders

By David J. Simourd. 2004

104 citations recorded by the source corpus.

Subfields: Clinical Psychology, Psychiatry and Mental health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/44540eb0eae784276773a7680eb3a1c6c80f2f4c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. J. Skeem
2. Maro Siranosian
3. Faye S. Taxman
4. Michael Caudy
5. J. Durso
6. Sarah M. Manchak
7. S. Belenko
8. K. Douglas

In Psychiatry and Mental health

1. Maro Siranosian
2. J. Skeem
3. Sarah M. Manchak
4. S. Belenko
5. K. Douglas

In Sociology and Political Science

1. J. Skeem
2. Maro Siranosian
3. Susan F. Turner
4. Faye S. Taxman
5. Michael Caudy
6. J. Durso
7. Sarah M. Manchak
8. S. Belenko

Citations in the corpus

• Development of the California Static Risk Assessment (CSRA): Recidivism Risk Prediction in the California Department of Corrections and Rehabilitation (2013, background): “Actuarial measures can include static factors, that is, factors that cannot be changed, such as criminal history, and/or dynamic factors, that is, factors that change over time, such as offender attitude or choice of companions (Simourd 2004).”
• Assessing Released Inmates for Substance-Abuse-Related Service Needs (2006, background): “…in the community; • have good psychometric properties; • are designed to serve as risk management and service linkage tools for parole officers; and • combine static and dynamic factors, covering different time periods, which are important for postrelease success and adjustment (Simourd, 2004).”
• How well do dynamic needs predict recidivism? Implications for risk assessment and risk reduction (2013, background): “The validity of the LSI-R total risk score has been well-established as a predictor of offender outcomes across a diverse array of samples and settings (Andrews & Bonta, 1995; Andrews et al., 2006; Girard & Wormith, 2004; Hollin & Palmer, 2006; Hollin, Palmer, & Clark, 2003; Holsinger, Lowenkamp, & ”
• Does Gender Moderate the Predictive Utility of the Level of Service Inventory—Revised (LSI-R) for Serious Violent Offenders? (2009, methodology): “A large body of research supports the predictive utility of the LSI-R for male offenders’ general recidivism (e.g., r = .44; Simourd, 2004) and convictions and revocations (e.”