Risk Assessment Essay ⋆ Criminal Justice Essay Examples ⋆ EssayEmpire

Risk assessment in the human services field is a structured or unstructured process whereby an attempt is made to predict harmful future behavior. Examples include predicting whether parents who are reported to child welfare authorities will subsequently maltreat their children, parolees will commit a violent crime upon release, and male batterers will injure or kill their intimate partner. Such assessments are used to help clinicians, judges, police officers, and other human services professionals make decisions that minimize the risk of harm to individuals and communities. In general, such assessments are presented as classifications of increasing risk ranging from low to high or very high.

There are three types of approaches to the assessment of risk: clinical judgment, consensus or expert driven measures, and actuarial measures. Clinical judgment is based on case study, intuitive judgment, or professional experience and is usually the least predictive form of risk assessment. There are many reasons for this shortcoming, but these reasons mainly center on the inability of human beings to accurately weigh and combine large amounts of disparate and often conflicting information. In essence, too much data can cause human services providers to base their decisions on information that is unrelated to actual risk.

Formal risk assessment measures are an attempt to address these concerns. Such tools are, essentially, lists of variables (e.g., caregiver and child characteristics or attributes, abuse circumstances, or environmental circumstances) that have been found to predict an outcome of interest (e.g., the initial occurrence or the recurrence of abuse). Consensus-based or expert-driven risk assessment instruments are tools that are compiled by experts in the field who draw upon previous research findings, clinical experience, or a combination of both, but an empirical study is usually not conducted to develop the instrument or to make sure that it is working well. Actuarial instruments, on the other hand, are developed by empirically identifying a set of risk factors with a strong statistical relationship to the behavioral outcome, and these factors are then weighted and combined to form an assessment tool that optimally classifies families or individuals according to the risk that they will exhibit the behavior. Although there has been a fairly long history of controversy in psychology about the use of actuarial versus clinical judgment and consensus-based prediction, actuarial tools have been found to clearly outperform both clinical judgment and consensus-based approaches.

Because predicting human behavior is inherently difficult, classifications of risk are generally used to inform rather than to determine service decisions. Risk assessment instruments are simply predictive tools. They are not designed to assess such things as family dynamics or other important functional issues, nor do they constitute treatment plans. Clinical and professional expertise is used to place risk assessment findings in context and to arrive at ways to prevent and/or minimize the consequences of future behavior.

Bibliography:

Dawes, R. M. (1994). House of cards: Psychology and psychotherapy built on myth. New York: Free Press.
Gambrill, E., & Shlonsky, A. (2000). Risk assessment in context. Children and Youth Services Review, 22(5–6), 813–837.
Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures. Psychology, Public Policy, and Law, 2(2), 293–323.
Rycus, J. S., & Hughes, R. C. (2003). Issues in risk assessment in child protective services: Policy white paper. Columbus, OH: North American Resource Center for Child Welfare, Center for Child Welfare Policy.
Shlonsky, A., & Wagner, D. (2005). The next step: Integrating actuarial risk assessment and clinical judgment into an evidence-based practice framework in CPS case management. Children and Youth Services Review, 27(3), 409–427.

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