Full text met HU account. In this article we report a study into the Dutch probation service about the question whether structured decision making about case management plans does or does not improve the quality of these plans, and subsequently improves the effectiveness of offender supervision. Two samples of nearly 300 case management plans each were compared. In the first sample a tool for risk/needs assessment was used to assess the risks and needs but decision making about the subsequent case management plan was not structured (RISc2-sample). In the second sample professionals used the same tool for risk and needs assessment but now it also contained a section for structured decision making about the case management plan (RISc3-sample). Results showed that in the RISc3-sample the quality of the plans was significantly better than in the RISc2-sample: a better match between criminogenic needs and goals, a better match between goals of the offender and goals in the plan, more focus on strengthening social bonds, and a better match between risk of recidivism and intensity of the plan. Some significant correlations between the quality of the plans and the effectiveness of offender supervision were found, indicating that improving case management plans by structured decision support indeed can contribute to probation practice.
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Abstract The aim of this cross-sectional study was to develop a Frailty at Risk Scale (FARS) incorporating ten well-known determinants of frailty: age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. In addition, a second aim was to develop an online calculator that can easily support healthcare professionals in determining the risk of frailty among community-dwelling older people. The FARS was developed using data of 373 people aged ≥ 75 years. The Tilburg Frailty Indicator (TFI) was used for assessing frailty. Multivariate logistic regression analysis showed that the determinants multimorbidity, unhealthy lifestyle, and ethnicity (ethnic minority) were the most important predictors. The area under the curve (AUC) of the model was 0.811 (optimism 0.019, 95% bootstrap CI = −0.029; 0.064). The FARS is offered on a Web site, so that it can be easily used by healthcare professionals, allowing quick intervention in promoting quality of life among community-dwelling older people.
Why are risk decisions sometimes rather irrational and biased than rational and effective? Can we educate and train vocational students and professionals in safety and security management to let them make smarter risk decisions? This paper starts with a theoretical and practical analysis. From research literature and theory we develop a two-phase process model of biased risk decision making, focussing on two critical professional competences: risk intelligence and risk skill. Risk intelligence applies to risk analysis on a mainly cognitive level, whereas risk skill covers the application of risk intelligence in the ultimate phase of risk decision making: whether or not a professional risk manager decides to intervene, how and how well. According to both phases of risk analysis and risk decision making the main problems are described and illustrated with examples from safety and security practice. It seems to be all about systematically biased reckoning and reasoning.
