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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Dementia is often diagnosed after emergency situations have emerged.The social network of elderly was studied in order to find out if persons or institutions could help to discover problems in an earlier phase. It was concluded that family is most important, and that information, rather than detection, is more helpful in the period preceding diagnosis. In: Journal of Clinical Nursing; 19 (1), 89
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Background: Survivors of lymphoma experience multiple challenges after treatment. However, a lack of knowledge of in-depth experiences of lymphoma survivors in early aftercare persists. Objective: To gain an in-depth understanding of the experiences of lymphoma survivors in early aftercare who have received an aftercare consultation based on evidence-based guideline recommendations, with an advanced practice nurse. Methods: This study used a narrative design. We recruited lymphoma survivors after a best-practice aftercare consultationwith an advanced practice nurse. A total of 22 lymphoma survivors and 9 partners participated. Data were collected through narrative interviews and analyzed according to thematic narrative analysis. Results: Six themes emerged: living and dealing with health consequences, coping with work and financial challenges, having a positive outlook and dealing with uncertainty, deriving strength from and experiencing tensions in relationships, getting through tough times in life, and receiving support from healthcare professionals. Conclusions: The stories of lymphoma survivors in early aftercare revealed their experiences of how they coped with a range of challenges in their personal lives. Choosing an aftercare trajectory based on an aftercare consultation that encourages patients to think about their issues, goals, and possible aftercare options may be useful for their transition from treatment to survivorship. Implications for practice: Survivors’ social support and self-management capabilities are important aspects to be addressed in cancer care. An aftercare consultation involving shared goal setting and care planning may help nurses provide personalized aftercare.
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Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
