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.
Risk assessment instruments are widely used to predict risk of adverse outcomes, such as violence or victimization, and to allocate resources for managing these risks among individuals involved in criminal justice and forensic mental health services. For risk assessment instruments to reach their full potential, they must be implemented with fidelity. A lack of information on administration fidelity hinders transparency about the implementation quality, as well as the interpretation of negative or inconclusive findings from predictive validity studies. The present study focuses on adherence, a dimension of fidelity. Adherence denotes the extent to which the risk assessment is completed according to the instrument’s guidelines. We developed an adherence measure, tailored to the ShortTerm Assessment of Risk and Treatability: Adolescent Version (START:AV), an evidence-based risk assessment instrument for adolescents. With the START:AV Adherence Rating Scale, we explored the degree to which 11 key features of the instrument were adhered to in 306 START:AVs forms, completed by 17 different evaluators in a Dutch residential youth care facility over a two-year period. Good to excellent interrater reliability was found for all adherence items. We identified differences in adherence scores on the various START:AV features, as well as significant improvement in adherence for those who attended a START:AV refresher workshop. Outcomes of risk assessment instruments potentially impact decision-making, for example, whether a youth’s secure placement should be extended. Therefore, we recommend fidelity monitoring to ensure the risk assessment practice was delivered as intended.
Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
