Risk assessment plays an important role in forensic mental health care. The way the conclusions of those risk assessments are communicated varies considerably across instruments. In an effort to make them more comparable, Hanson, R. K., Bourgon, G., McGrath, R., Kroner, D. D., Amora, D. A., Thomas, S. S., & Tavarez, L. P. [2017. A five-level risk and needs system: Maximizing assessment results in corrections through the development of a common language. The Council of State Governments Justice Center. https:// csgjusticecenter.org/wp-content/uploads/2017/01/A-Five-Level-Risk-and-Needs-system_Report.pdf] developed the Five-Level Risk and Needs System, placing the conclusions of different instruments along five theoretically meaningful levels. The current study explores a Five-Level Risk and Needs system for violent recidivism to which the numerical codings of the HCR-20 Version 2 and its successor, the HCR-20V3 are calibrated, using a combined sample from six previous studies for the HCR-20 Version 2 (n = 411 males with a violent index offence) and a pilot sample for the HCR-20V3 (n = 66 males with a violent index offence). Baselines for the five levels were defined by a combination of theoretical (e.g. expert meetings) and empirical (e.g. literature review) considerations. The calibration of the HCR-20 Version 2 was able to detect four levels, from a combined level I/II to an adjusted level V. The provisional calibration of the HCR-20V3 showed a substantial overlap with the HCR-20 Version 2, with each level boundary having a 2-point difference. Implications for practice and future research are discussed.
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The purpose of this study is to analyze the relationship between sustainable performance and risk management, whereby sustainability (innovation), interdisciplinarity and leadership give new insights into the traditional perspectives on performance and risk management in the field of accounting and finance.
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Purpose: The purpose of this study is to find determinants about risk resilience and develop a new risk resilience approach for (agricultural) enterprises. This approach creates the ability to respond resiliently to major environmental challenges and changes in the short term and adjust the management of the organization, and to learn and transform to adapt to the new environment in the long term while creating multiple value creation. Design/methodology: The authors present a new risk resilience approach for multiple value creation of (agricultural) enterprises, which consists of a main process starting with strategy design, followed by an environmental analysis, stakeholder collaboration, implement ESG goals, defining risk expose & response options, and report, learn & evaluate. In each step the organizational perspective, as well as the value chain/area perspective is considered and aligned. The authors have used focus groups and analysed literature from and outside the field of finance and accounting, to design this new approach. Findings: Researchers propose a new risk resilience approach for (agricultural) enterprises, based on a narrative about transforming to multiple value creation, founded determinants of risk resilience, competitive advantage and agricultural resilience. Originality and value: This study contributes by conceptualizing risk resilience for (agricultural) enterprises, by looking through a lens of multiple value creation in a dynamic context and based on insights from different fields, actual ESG knowledge, and determinants for risk resilience, competitive advantage and agricultural resilience.
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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.
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There is emerging evidence that the performance of risk assessment instruments is weaker when used for clinical decision‐making than for research purposes. For instance, research has found lower agreement between evaluators when the risk assessments are conducted during routine practice. We examined the field interrater reliability of the Short‐Term Assessment of Risk and Treatability: Adolescent Version (START:AV). Clinicians in a Dutch secure youth care facility completed START:AV assessments as part of the treatment routine. Consistent with previous literature, interrater reliability of the items and total scores was lower than previously reported in non‐field studies. Nevertheless, moderate to good interrater reliability was found for final risk judgments on most adverse outcomes. Field studies provide insights into the actual performance of structured risk assessment in real‐world settings, exposing factors that affect reliability. This information is relevant for those who wish to implement structured risk assessment with a level of reliability that is defensible considering the high stakes.
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Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
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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.
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In this study, growth trajectories (from admission until unconditional release) of crime-related dynamic risk factors were investigated in a sample of Dutch forensic patients (N = 317), using latent growth curve modeling. After testing the unconditional model, three predictors were added: first-time offender versus recidivist, age, and treatment duration. Postanalyses were chi-square difference tests, t tests, and analyses of variance (ANOVAs) to assess differences in trajectories. Overall, on scale level, a decrease of risk factors over time was found. The predictors showed no significant slope differences although age and treatment duration differed significantly at some time points. The oldest age group performed worse, especially at later time points. Treatment duration effects were found at the second time point. Our results that forensic patients show a decrease in crime-related risk factors may indicate that treatment is effective. This study also found differences in growth rates, indicating the effect of individual differences
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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.
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Vietnamese farmed pangasius products have experienced major price fluctuations over numerous crop cycles exposing this important export commodity to risk and uncertainty. This study focuses on two distinct areas of risk. First, this study seeks to measures the farm-gate price volatility, explores farmers’ perceptions about the price volatility risk related to the input sourcing and output selling activities, and quantifies the effectiveness of price volatility risk management strategies. Second, the relationships between farm and farmer socioeconomic characteristics, perceptions about the price volatility risk and management strategies are also examined. To respond to these research questions, a focus group workshop was conducted with 29 stakeholders and a structured survey with 92 farmers located in the provinces of An Giang, Can Tho, and Dong Thap of the Vietnamese Mekong Delta. Results illustrated that the pangasius farm-gate price fluctuates significantly over time. Vietnamese pangasius farmers are mainly concerned about the volatilities of input and output prices, additionally with the instability of the volume input supply, instability in demand volume, and weak legislation on sale contracts. Results further indicate that price volatility risk management strategies are less effective in practically protecting farmers against price volatility risks. Farmers with higher education knowledge opt for fully integrated farms and are less concerned about the output price volatility risk. Promoting risk-sharing schemes such as contract farms or insurance for farmers with higher educational levels and being accessible to credit could support Vietnamese pangasius farmers toward increasing the sustainability of pangasius production.
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