In this paper, we explore the conceptual problems that arise when using network analysis in person-centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises , i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that perspectival reasoning can make more explicit what questions personalized network models can address in PCC, given their boundaries.
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http://dx.doi.org/10.3389/fpsyt.2022.925187 | DOI Listing |
J Cardiothorac Surg
January 2025
Department of Paediatrics, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil Vidyapeeth, Maharashtra, Pune, 411018, India.
Background: Proton pump inhibitors (PPIs) are commonly used for managing gastroesophageal disorders but concerns about their potential association with increased stroke risk have emerged, especially among patients with pre-existing cardiovascular conditions such as acute coronary syndrome (ACS). This systematic review and meta-analysis aim to assess the risk of stroke associated with PPI use, stratified by the presence or absence of pre-existing CVD.
Methods: This review was conducted following the PRISMA guidelines and included studies up to March 2024 from PubMed, Embase, and Web of Science.
BMC Public Health
January 2025
Economics Department, Macquarie University, Sydney, Australia.
Background: Health is the cornerstone of individual well-being and a vital factor in socioeconomic development. In an increasingly digitalized world, digital literacy has emerged as one of the indispensable abilities, which not only pertains to an individual's capacity to acquire, analyze, evaluate, and utilize information but also profoundly influences their health behaviours, health decisions, and overall well-being. This paper uses the 2020 China Family Panel Studies (CFPS) data to explore digital literacy's impact on individuals' health.
View Article and Find Full Text PDFSci Rep
January 2025
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK.
In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP.
View Article and Find Full Text PDFSci Rep
January 2025
Office for the Advancement of Educational Information, Chengdu Normal University, Chengdu, 610000, China.
In the training of teacher students, simulated teaching is a key method for enhancing teaching skills. However, traditional evaluations of simulated teaching typically rely on direct teacher involvement and guidance, increasing teachers' workload and limiting the opportunities for teacher students to practice independently. This paper introduces a Retrieval-Augmented Generation (RAG) framework constructed using various open-source tools (such as FastChat for model inference and Whisper for speech-to-text) combined with a local large language model (LLM) for audio analysis of simulated teaching.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.
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