This introduction to the National Guidelines for Post-Conviction Risk and Needs Assessment identifies the needs addressed by the guidelines.
There are valid and growing concerns about the accuracy, fairness, transparency, and communication in the use of risk and needs assessment with convicted offenders. Criminal justice agencies have not been given the guidance they need to communicate the strengths and limitations of risk and needs assessment. In addition, those who are being assessed rarely receive information on these assessments, their accuracy, and how their findings will be used in the offender’s programming and management. The guidelines presented in this report are intended to address these gaps in the content, accuracy, and use of risk and needs assessment instruments. These guidelines are a new resource from the U.S. Department of Justice’s Office of Justice Programs’ Bureau of Justice Assistance (BJA) and the Council of State Governments Justice Center. They address identified gaps in the content and administration of risk and needs assessment instruments. The guidelines were developed under the advisement of a national group of researchers, risk and needs assessment instrument developers, practitioners, and leaders in the field. The guidelines prioritize accuracy, fairness, and transparency in the communication and use of risk and needs assessment. From this introductory page, online access is provided to the Executive Summary of the guidelines and the Companion Guide. Online access is also provided to the Council of State Government’s Justice Center’s site for a self-assessment that enables an agency to determine how closely it is currently following the provided guidelines.
Similar Publications
- Prosecuting Cold Cases Using DNA
- The Unintended Effects of the Prison Rape Elimination Act (PREA) in a Maximum-Security Prison for Women: Weaponization, Bullying, and Compulsory Heterosexuality
- Police Officer Attitudes toward Pre-arrest Behavioral Health Diversion Programs: Identifying Determinants of Support for Deflection Using a Machine Learning Method