Aims and objectives. The Forensic Early Warning Signs of Aggression Inventory (FESAI) was developed to assist nurses and patients in identifying early warning signs and constructing individual early detection plans (EDP) for the prevention of aggressive incidents. The aims of this research were as follows: First, to study the prevalence of early warning signs of aggression, measured with the FESAI, in a sample of forensic patients, and second, to explore whether there are any types of warning signs typical of diagnostic subgroups or offender subgroups. Background. Reconstructing patients’ changes in behaviour prior to aggressive incidents may contribute to identify early warning signs specific to the individual patient. The EDP comprises an early intervention strategy suggested by the patient and approved by the nurses. Implementation of EDP may enhance efficient risk assessment and management. Design. An explorative design was used to review existing records and to monitor frequencies of early warning signs. Methods. Early detection plans of 171 patients from two forensic hospital wards were examined. Frequency distributions were estimated by recording the early warning signs on the FESAI. Rank order correlation analyses were conducted to compare diagnostic subgroups and offender subgroups concerning types and frequencies of warning signs. Results. The FESAI categories with the highest frequency rank were the following: (1) anger, (2) social withdrawal, (3) superficial contact and (4) non-aggressive antisocial behaviour. There were no significant differences between subgroups of patients concerning the ranks of the four categories of early warning signs. Conclusion. The results suggest that the FESAI covers very well the wide variety of occurred warning signs reported in the EDPs. No group profiles of warning signs were found to be specific to diagnosis or offence type. Relevance to clinical practice. Applying the FESAI to develop individual EDPs appears to be a promising approach to enhance risk assessment and management.
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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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Objectives: Malnutrition is associated with a twofold higher risk of dying in patients with tuberculosis (TB) and considered an important potentially reversible risk factor for failure of TB treatment. The construct of malnutrition has three domains: intake or uptake of nutrition; body composition and physical and cognitive function. The objectives of this systematic review are to identify malnutrition assessment methods, and to quantify how malnutrition assessment methods capture the international consensus definition for malnutrition, in patients with TB.Design: Different assessment methods were identified. We determined the extent of capturing of the three domains of malnutrition, that is, intake or uptake of nutrition, body composition and physical and cognitive function.Results: Seventeen malnutrition assessment methods were identified in 69 included studies. In 53/69 (77%) of studies, body mass index was used as the only malnutrition assessment method. Three out of 69 studies (4%) used a method that captured all three domains of malnutrition.Conclusions: Our study focused on published articles. Implementation of new criteria takes time, which may take longer than the period covered by this review. Most patients with TB are assessed for only one aspect of the conceptual definition of malnutrition. The use of international consensus criteria is recommended to establish uniform diagnostics and treatment of malnutrition.
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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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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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Previous studies have found evidence for a relationship between debt and crime, and for problems in childhood, education, work, and mental and physical health as underlying risk factors. However, insight into the interplay between these possible risk factors is limited. Therefore, a mixed methods approach was applied by both creating a quantitative Gaussian Graphical Model (GGM) and conducting qualitative analyses on 250 client files including risk assessment data from the Dutch probation service, to gain more specific insight into the interaction between potential risk factors. The results show that debt is strongly related to criminal behavior and problems in many life domains for most probation clients. Debt seems to be a direct risk factor for crime, but debt and crime also appear to be highly interrelated as part of a complex interplay of risk factors. The most frequently rated factors – limited or incomplete education, no job and related lack of income, and mental and physical health problems – are highly interwoven and increase the risk of both debt and crime. The findings stress the importance of paying attention to and using interventions focusing on strongly related crime risk factors, including debt, and their complex interplay, to supervise probation clients effectively.
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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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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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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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Thirty to sixty per cent of older patients experience functional decline after hospitalisation, associated with an increase in dependence, readmission, nursing home placement and mortality. First step in prevention is the identification of patients at risk. The objective of this study is to develop and validate a prediction model to assess the risk of functional decline in older hospitalised patients.
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