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Predictors of Discharge from Hospital to Supported Accommodation and Support Needs Once in Supported Accommodation for People with Serious Mental Illness in Scotland: A Linked National Dataset Study
Get PDFBackground. Many individuals with serious mental illness live in supported accommodation. Decisions regarding type of supported accommodation required and level of support to meet individual’s needs are crucial for continuing rehabilitation and recovery following admission to hospital. This study aimed to identify personal and contextual predictive factors for (1) discharge from hospital to different levels of supported accommodation and (2) self-directed support needs of individuals with serious mental illness once they are in supported accommodation in Scotland. Method. Linked data from the Scottish Morbidity Record-Scottish Mental Health and Inpatient Day Case Section and the Scottish Government Social Care Survey were analysed using multinomial regression and multivariable logistic regression to identify personal and contextual factors associated with accommodation destination at the time of discharge and four self-directed support needs: personal care; domestic care; healthcare; and social, educational, and recreational. Results. Personal factors (age and having a diagnosis of schizophrenia, schizotypal, or delusional disorder) were associated with individuals moving to supported accommodation with higher levels of support. One contextual factor, compulsory detention when admitted to hospital, decreased the likelihood of moving to any type of supported accommodation. The personal and contextual factors associated with identified self-directed support needs varied by need. Support provided by the local authority was associated with all self-directed support needs, with having a diagnosis of schizophrenia, schizotypal, or delusional disorder associated with identifying domestic care, healthcare, and social, educational, and recreational needs, while living in the most deprived areas was associated with identifying healthcare needs. Advancing age and being compulsorily detained decreased the likelihood of identifying social, educational, and recreational needs. Conclusion. The study highlights that older men with a diagnosis of schizophrenia, schizotypal, or delusional disorder require higher levels of support upon discharge from hospital. When living in supported accommodation, having this diagnosis increases the likelihood of identifying support with looking after the home, looking after their health, and social and recreational activities; however, being older decreases the likelihood of identifying support with social and recreational activities
Engineered Timber Products
No full textEngineered timber products can surpass the natural limits of timber strength and span, by combining smaller timber sections with adhesives or fasteners to form larger elements
From Digital Inclusion to Digital Transformation in the Prevention of Drug-Related Deaths in Scotland: Qualitative Study
Get PDFBackground:Globally, drug-related deaths (DRDs) are increasing, posing a significant challenge. Scotland has the highest DRD rate in Europe and one of the highest globally. The Scottish Government launched the Digital Lifelines Scotland (DLS) program to increase the provision of digital technology in harm reduction services and other support services. Digital technology responses to DRDs can include education through digital platforms, improved access to treatment and support via telehealth and mobile apps, analysis of data to identify risk factors, and the use of digital tools for naloxone distribution. However, digital technology should be integrated into a comprehensive approach that increases access to services and addresses underlying causes. Digital transformation could enhance harm reduction service and support, but challenges must be addressed for successful implementation. The DLS program aims to enhance digital inclusion and improve health outcomes for people who use or are affected by drug use to reduce the risk of DRDs.Objective:This study aims to explore the role of digital technology as an enabler and supporter in enhancing existing services and innovating new solutions, rather than being a stand-alone solution. Specifically focusing on individuals who use drugs, the research investigates the potential of digital inclusion and technology provision for preventing DRDs within the context of the DLS program.Methods:Semistructured interviews were conducted with 47 people: 21 (45%) service users, 14 (30%) service providers, and 12 (26%) program staff who were all involved in DLS. Interviews were audio recorded, transcribed, and then coded. Analysis was done in three phases: (1) thematic analysis of interview data to identify the benefits of digital technologies in this sector; (2) identification of the challenges and enablers of using digital technologies using the Technology, People, Organizations, and Macroenvironment conceptual framework; and (3) mapping digital technology provision to services offered to understand the extent of digital transformation of the field.Results:Participants identified increased connectivity, enhanced access to services, and improved well-being as key benefits. Digital devices facilitated social connections, alleviated loneliness, and fostered a sense of community. Devices enabled engagement with services and support workers, providing better access to resources. In addition, digital technology was perceived as a preventive measure to reduce harmful drug use. Lack of technical knowledge, organizational constraints, and usability challenges, including device preferences and security issues, were identified.Conclusions:The study found that digital inclusion through the provision of devices and connections has the potential to enhance support in the harm reduction sector. However, it highlighted the limitations of existing digital inclusion programs in achieving comprehensive digital transformation. To progress, there is a need for sustained engagement, cultural change, and economic considerations to overcome barriers
The oxidative potential of nanomaterials: an optimized high-throughput protocol and interlaboratory comparison for the ferric reducing ability of serum (FRAS) assay
Get PDFSuccessful implementation of Safe and Sustainable by Design (SSbD) and grouping approaches requires simple, reliable, and cost-effective assays to facilitate hazard screening at early stages of product development. Especially for nanomaterials (NMs), which exist in many different forms, efficient hazard screening is of utmost importance. Oxidative potential (OP), which is the ability of a substance to induce reactive oxygen species (ROS), is an important indicator of the potential to induce oxidative damage and oxidative stress. A frequently used assay to measure OP of NMs is the ferric reducing ability of serum (FRAS) assay. Although the widely used cuvette-based FRAS protocol is considered a robust assay, its low throughput makes the screening of multiple materials challenging. Here, we adapt the original cuvette-based FRAS assay protocol, into a 96-well format and thereby improve its user-friendliness, simplicity, and screening capacity. The adapted protocol allows for the screening of multiple NMs per plate, and multiple plates per day, where the original protocol allows for the screening of one NM dose-range per day. When comparing the two protocols, the adapted protocol showed slightly decreased assay precision as compared to the original protocol. The results obtained with the adapted protocol were compared using eight reference NMs in an interlaboratory study and showed acceptably low intra- and interlaboratory variation. We conclude that the adapted FRAS assay protocol is suitable to be used for hazard screening to facilitate SSbD and grouping approaches
Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings
Get PDFPre-trained language models are a highly effective source of knowledge transfer for natural language processing tasks, as their development represents an investment of resources beyond the reach of most researchers and end users. The widespread availability of such easily adaptable resources has enabled high levels of performance, which is especially valuable for low-resource language users who have typically been overlooked when it comes to NLP applications. However, these models introduce vulnerabilities in NLP toolchains, since they may prove vulnerable to attacks from malicious actors with access to the data used for downstream training. By perturbing instances from the training set, such attacks seek to undermine model capabilities and produce radically different outcomes during inference. We show that adversarial data manipulation has a severe effect on model performance, with BERT's performance dropping by more than 30% on average across all tasks at a poisoning ratio greater than 50%. Additionally, we conduct the first evaluation of this kind in the Basque language domain, establishing the vulnerability of low-resource models to the same form of attack
Fiddler crabs can feel more than we think: the influence of neighbors on the activities of the fiddler crab Leptuca uruguayensis
Get PDFFiddler crabs have been used as model organisms in many laboratory and field studies. In their natural environment, social interaction with other fiddler crabs (conspecific or heterospecific) is recurrent, but manipulative studies involving these crabs as models are often performed with isolated individuals. The isolation of an animal can interfere in the behaviors recorded as response variables. Thus, the aim of this study was to evaluate whether the presence of other individuals affects the performance of behaviors of fiddler crabs Leptuca uruguayensis. We tested two hypotheses in the field: (1) the visual stimulus of the crab assemblage affects the activity of male fiddler crabs; and (2) the presence of other conspecific affects the activity of male fiddler crabs depending on the sexes of the individuals present. We found the activities of L. uruguayensis males mediated by social interactions does not depend exclusively on visual stimuli. Physical interaction with other conspecifics of both sexes enables the perception of stimuli which can influence the waving behavior of L. uruguayensis males. We suggest that behavioral studies with this model should consider the presence of other individuals. Understanding the behavioral complexity of a model organism contributes to more robust experiments with greater control of interfering variables
We all care, ALL the time
Get PDFCare does not happen in a vacuum, including nursing care. With this in mind, we—Jess, Jane, Jamie, Brandon, and Eva1—partnered with critical posthuman scholars Goda Klumbytė from Kassel University in Germany and Dr. Kay Sidebottom from Stirling University in Scotland for a discussion of care. Goda's research straddles critical algorithm studies, systems design, and feminist theory, drawing together these critical perspectives with applied informatics. Kay focuses on posthuman approaches to curriculum and education, affirmative ethics, and how philosophy and art can be used to reimagine education. Although on the surface, their scholarship appears to be exogenous to nursing, critical posthumanism emphasizes the convergence of thinking inter-, trans-, anti-, and postdisciplinarity (Braidotti, 2019). Features that unite the work of nursing with Goda and Kay's foci include the explorations of bodies, control, education, and labor. This points to mutual interests along the axes of critical analyses of humanism, and moving toward more transversal methodologies and posthumanities praxes when it comes to care. Specifically, we are interested in the potentiality of transdisciplinary methodologies of caring and care that are situated outside of capitalist and state enclosures which include all human, other-than-human, more-than-human, and nonhuman matter. These ideas are important for nurses and non-nurses alike as everybody is, has, or will be in need of both nursing and other forms of care. We all care all the time. Nursing sometimes lays claim to care as proprietary, under its sole purview, happening in acute care spaces, within the nurse/patient dyad and centered on neoliberalized individualistic assumptions (Dillard-Wright et al., 2020; Smith et al., 2022). We challenge this notion. Our discussion begins with the politics of care, and the idea that care is overdetermined, exploring who gets to define care. We then turn to the time-space of care, which is multiple. We conclude with considerations of how care is situated and contextual
Translation and validation of the Japanese version of the Birth Satisfaction Scale‐Revised
Get PDFAim: This study aimed to develop a Japanese version of the Birth Satisfaction Scale-Revised and evaluate its reliability and validity.Methods: After translating the Birth Satisfaction Scale-Revised into Japanese, we conducted an Internet-based cross-sectional study with 445 Japanesespeaking women within 2 months of childbirth. Of these, 98 participated in the retest 1 month later. Data were analyzed using the COSMIN study design checklist for patient-reported outcome measurement instruments. Content validity was evaluated through cognitive debriefing during the translation process into Japanese. Confirmatory factor analysis was conducted to verify structural and cross-cultural validities. For hypothesis testing, we tested correlations with existing measures for convergent and divergent validities, and for known-group discriminant validity, we made comparisons between types of childbirth. Internal consistency was calculated using Cronbach's α, and test–retest reliability was evaluated using the intraclass correlation coefficient.Results: For the Japanese-Birth Satisfaction Scale-Revised, the established threefactor model fit poorly, whereas the four-factor model fit better. Full metric invariance was observed in both the nulliparous and multiparous groups. Good convergent, divergent, and known-group discriminant validities and test–retest reliability were established. Internal consistency observations were suboptimal; however for vaginal childbirth, the Cronbach's α of the total score was .71.Conclusions: The Japanese-Birth Satisfaction Scale-Revised is a valid and reliable scale, with the exception of internal consistency that requires further investigation. If limited to vaginal childbirth, research, clinical applications, and international comparisons can be drawn
MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer
No full textThe proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defending against malware. Numerous approaches employ supervised learning techniques, specifically Convolutional Neural Networks (CNNs), to train feature extractors. However, acquiring a substantial quantity of labled samples incurs significant expenses, and relying solely on CNNs as feature extractors may result in restricted local receptive fields, consequently compromising the preservation of crucial features. In order to address these constraints, we propose an effective malware classification approach, denoted as MalSort, which leverages the masked self-supervised framework with Swin Transformer. Initially, each instance of malware is transformed into a color image. Furthermore, the Swin Transformer self-supervised framework is utilized to extract multi-scale key feature vectors from a randomly masked partial color image, while the prediction module is employed to predict the masked image. Ultimately, the pre-trained encoder is fine-tuned using the malware dataset to effectively carry out a malware classification task. Our MalSort exhibits a reduced reliance on labeled data samples during the training phase, thereby obviating the necessity for extensive amounts of labeled data. Consequently, the MalSort conserves hardware resources and improve its training efficiency. The experimental results indicate that the MalSort outperforms existing models by achieving a classification accuracy of 97.85%, a recall of 97.63%, a precision of 97.85%, and an F1-score of 97.85% on the BIG2015 dataset. Similarly, on the Malimg dataset, the model achieves percentages of 98.28%, 98.18%, 98.19%, and 98.28% for classification accuracy, recall, precision, and F1-score, respectively
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