Information Network Research Articles

Network Types for End-Of-Life Care and Home Deaths Among Older Adults in Europe During COVID-19 Pandemic.

The study identified care network types comprising informal and formal care providers during the end-of-life period, and examined their relationship with home deaths. End-of-life interviews were conducted with proxies during the two waves of the COVID-19 pandemic in the Survey of Health, Ageing and Retirement in Europe (SHARE). The sample included 486 participants who passed away during the pandemic and received care during their final year. Latent Class Analysis identified three care network types: "mixed-care", mostly consisting of adult children in an informal network; "spouse", dominated by spouses with adult children and formal caregivers; and "professional", mainly with formal caregivers and some informal support. Patients with mixed-care were more likely to die at home compared to those with professional networks. Informal care, particularly from adult children, played a crucial role in end-of-life care. Policymakers should support adult children in end-of-life care and promote effective models that integrate formal care services.

Longitudinal associations between informal caring, social network, and psychological distress among adolescents and young adults: modelling within-person effects

BackgroundInformal caring is associated with mental health deterioration among young people and impacts their help-seeking ability. Social network can provide social support and mitigate the impact of informal care. However, young carers may avoid identification and withdraw from social networks. Evidence regarding the reciprocal associations between caring, social network, and mental health is scarce. We aimed to investigate the directionality and specificity of the associations among the three factors in young people.MethodsThis study used three consecutive assessment data (2021–2023; T0–T2) from the Japan COVID-19 and Society Internet Survey. We included 5539 young persons aged ≤ 25 years and 25,445 adults aged 26–59 years. Social network was measured using the Lubben Social Network Scale. Psychological distress was evaluated using the Kessler Psychological Distress Scale. Caring status was retrospectively reported at T2. We employed a random intercept cross-lagged model to detect within-person prospective associations between informal caring, social network, and psychological distress.ResultsYoung persons showed significant directional relationships from increased social network and psychological distress at T0 to increased likelihood of caring at T1 (standardised coefficient: 0.131 and 0.176, respectively; 95% confidence interval, 0.015–0.247 and 0.071–0.282, respectively). Adults aged 26–59 years showed a reverse relationship from caring to increased psychological distress both from T0 to T1 (0.061, 0.009–0.112) and from T1 to T2 (0.042, 0.000–0.084).ConclusionsIncreased psychological distress and social network preceded the onset of informal caring among young persons. Incorporating psychological distress assessment may benefit the early identification of and support for young carers. The long-term interplay between social networking and informal caring needs further clarification.

Comparative analysis of land-based vs. water-based balance training on quality of life and physical and psychological deficits in athletes with chronic ankle instability: a randomized controlled trial

BackgroundAquatic training is known for its effective and gentle rehabilitation benefits, but its impact on athletes with chronic ankle instability (CAI) remains underexplored. This study compares the effects of water-based and land-based balance training on functional performance, dynamic balance, fear of reinjury, and quality of life in athletes with CAI.MethodsForty-one athletes with chronic ankle instability (CAI) were randomly assigned to water-based (WBBE, n = 21) or land-based (LBBE, n = 20) balance exercise groups, completing 24 sessions of 30–45 min over 8 weeks. Assessments before and after the interventions included functional ankle instability (CAIT), kinesiophobia (TSK-17), quality of life (SF-36), dynamic postural control (Y Balance Test), and functional performance (Figure-8 hop and single-limb side-hop tests). Perceived treatment effects were measured using the Global Rating of Change (GROC) post-intervention. Data were analyzed using mixed-design ANOVA (P ≤ 0.05).ResultsThe statistical analysis of the study revealed no significant time × group interaction effects for CAIT scores, kinesiophobia scores, the Psychological Quality of Life (QoL) Component, or SEBT scores (p > 0.05). The LBBE group showed significant improvements in functional tasks, specifically in F8H and SLSH scores, compared to the WBBE group (p < 0.05), while the WBBE group had better outcomes in overall Quality of Life and the Physical QoL Component than the LBBE group (p < 0.05).ConclusionBoth land-based and water-based balance exercises benefit athletes with CAI, with land-based exercises improving functional performance and water-based exercises enhancing physical and overall Quality of Life. A flexible rehabilitation program combining both approaches can optimize recovery, addressing specific needs, even though no significant differences were found in ankle stability, kinesiophobia, psychological Quality of Life, and dynamic balance between the two methods.Trial registrationThis study was prospectively registered with the University Hospital Medical Information Network Clinical Trial Registry under Clinical Trials UMIN000051746 on July 29, 2023.

Road coverage as demand metric for ambulance allocation.

Ambulances must be strategically placed to ensure timely patient care and save lives. The allocation problem considered in the current paper optimally distributes a fixed number of ambulances over predetermined bases with limited capacity. Ambulance allocation problems are usually solved through historical demand. In such cases, researchers process call record data that is shared by ambulance service providers. This paper proposes an alternative demand metric, namely the meters of covered road. Road network information is widely and publicly available, making it easily accessible. We demonstrate for a real ambulance region that the road coverage demand metric performs similarly to the historical call record metric in the case of static allocation, and that it outperforms when dynamic ambulance management is used.

Triadic balance and network evolution in predictive models of signed networks

This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving patterns of triadic structures, its effects on overall network dynamics remain underexplored. Existing research often neglects the interaction between micro-level balancing mechanisms and overall network behavior. To bridge this gap, we develop a method for detecting dynamic triadic structures in signed networks, categorizing triangle transformations over two consecutive periods into formation and breakage. We analyze the impact of these structures on temporal network evolution by incorporating them into exponential random graph models across five networks of varying size, density, and directionality. To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. These findings highlight the importance of considering the triadic transformation processes of balance triangles in studying temporal networks, validated across diverse datasets, warranting further research.

The influence of prosthetic treatments and implant-supported prostheses on posterior mandibular ridge atrophy: a retrospective cohort study

AimsBone preservation is a requirement for long-term stability of dental prostheses, which is all the more important in the posterior mandible, given the particular challenges these areas pose to prosthetic treatment. The objective was to investigate the implications of different prostheses on the atrophy of posterior alveolar bone after tooth loss.Materials and methodsA total of 457 treatment cases were retrieved from the medical documentation and information network. Baseline panoramic radiographs taken at prosthetic delivery and follow-up radiographs (≥ 3 years post-treatment) were available for analysis, with a mean follow-up duration of 6.42 ± 2.37 years. Posterior mandibular bone height in these radiographs was compared using one-way ANOVA followed by the Tukey post hoc test for statistical analysis.ResultsSites supporting fixed prostheses commonly used in partially edentulous mandibles exhibited less bone atrophy compared to sites with clasp- or telescope-retained removable partial dentures (RPDs) (p < 0.001). Furthermore, sites with clasp-retained RPDs and telescope-retained RPDs demonstrated less bone atrophy when distal support was present (p < 0.001). In edentulous mandibles, bar-retained overdentures supported by four implants exhibited significantly less bone atrophy compared to mucosa-supported complete dentures, bar-retained overdentures supported by two implants, or overdentures retained by Locator abutments on two implants (p < 0.001).ConclusionBone atrophy in the posterior mandible should be expected to vary greatly with prosthetic designs. Long-term stability is possible with fixed partial prostheses, RPDs are less favorable in the absence of a distal support and bar-retained overdentures on four or more implants exhibited the lowest bone atrophy in fully edentulous mandibles.Clinical trial numberNot applicable – retrospective analysis.

The Effect of Weed Control with Pre-Emergence Herbicides on the Yield Level of Mung Bean Yield.

The mung bean (Vigna radiata (Linn) Wilczek.) is a major grain crop in China, but its yield is significantly impacted by weeds. However, no pre-emergence herbicides are registered for mung bean fields in the China Pesticide Information Network. Screening for efficient and safe pre-emergence herbicides could improve mung bean production efficiency. In this study, six pre-emergence herbicides were selected: 480 g/L alachlor (1935.00 g a.i ha-1), 720 g/L metolachlor (1620.00 g a.i ha-1), 100 g/L imazethapyr (100.50 g a.i ha-1), 338 g/L oxadiazon (507.00 g a.i ha-1), 330 g/L pendimethalin (144.00 g a.i ha-1), and 480 g/L trifluralin (720.00 g a.i ha-1). Through Petri dish screening, a spraying treatment was carried out before seed germination. By measuring the root length and shoot length, imazethapyr (100.50 g a.i ha-1) and oxadiazon (507.00 g a.i ha-1) were screened out. They were applied to potted plants and sprayed after sowing. The results showed that imazethapyr (100.50 g a.i. ha-1) and oxadiazon (507.00 g a.i. ha-1) had no inhibitory effect on the growth and development of the mung bean. Subsequently, experiments were conducted with imazethapyr (100.50 g a.i ha-1) and oxadiazon (507.00 g a.i ha-1) applied in the field. Compared to the control, under imazethapyr (100.50 g a.i ha-1) and oxadiazon (507.00 g a.i ha-1), the agronomic traits, photosynthetic pigment content, yield, and yield components were not inhibited; the activities of superoxide dismutase, peroxidase, and catalase were increased; and gas exchange and chlorophyll fluorescence were not inhibited. In addition, soil urease activity decreased and soil invertase and alkaline phosphatase activity increased after 60 d of treatment. In summary, imazethapyr and oxadiazon can effectively control weeds and increase mung bean yield. The purpose of this study is to screen out safe and efficient pre-emergence herbicides suitable for the Shanxi mung bean, which is of great significance due to its large-scale planting industrialization and the development of advantageous industries.

Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection.

To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level. Furthermore, the pre-trained networks achieved higher accuracies on defect classification compared to the self-built network, with ResNet-50 displaying higher accuracy. The inspection system consistently obtained fast and accurate surface classifications because it imposed OK classification on models trained with images from both illumination modes. The obtained surface information was then successfully sent to a server to be forwarded to a graphical user interface for visualization. The developed system showed considerable robustness, demonstrating its potential as an efficient tool for industrial quality control.

Projectile points across the Northern Rocky Mountain front: the Billy Big Spring Site (Blackfeet Reservation, Montana)

In the recent past, the Front Ranges of the Northern Rocky Mountains formed a permeable boundary that people repeatedly crossed for trade, subsistence, or warfare, and where people occupying different parts of western North America interacted. In this paper we apply a landscape approach to study pre-contact projectile point morphologies and raw materials, and related changes in the geographic affiliations of peoples using the Billy Big Spring Site, located near the Front Ranges in the Blackfeet Indian Reservation of Montana. This site contains numerous points recovered in chronostratigraphic contexts ranging in age from Late Paleoindian to Late Precontact. The sample includes types and raw materials alternatively typical of the Rockies/Plateau, the Front Ranges, and/or the Plains. Variation in the projectile point record at Billy Big Spring is consistent with the notion that, as observed in the recent past, information networks spanned a wide geographic area that englobed the Plains, the Front Ranges, and the Plateau/Rockies during most of the pre-contact period. Exceptions and changes in the geographic affiliations of people using the site can in turn be related to major environmental and demographic events that occurred in the region.

Impact of information disparity between individual investors on profits of meme stocks using an artificial market simulation approach

The growth of social media recently has made individual investors more reliant on online media for information. This trend significantly affects investor behavior and information disparity. For instance, social media can lead to the phenomenon of “meme stocks," in which stock prices rapidly rise and fall. Despite increasing interest in meme stocks, few studies have focused on the information disparity. In this study, we model meme stocks focusing on investors who are influenced by social network information, leading to information spread. We combine the Susceptible-Infectious-Recovered (SIR) model with a network model in an artificial market simulation to examine information disparity among individual investors. Our findings show that conventional investors generally profit, whereas social media investors incur losses. Additionally, the profits of social media investors are heavily affected by their network structure and their network distance from the information source. The results highlight the issue of information inequality in financial markets and offer important guidance for policymakers.

Seizure network characterization by functional connectivity mapping and manipulation.

Despite the availability of various anti-seizure medications, nearly 1/3 of epilepsy patients experience drug-resistant seizures. These patients are left with invasive surgical options that do not guarantee seizure remission. The development of novel treatment options depends on elucidating the complex biology of seizures and brain networks. We aimed to develop an experimental paradigm that uses anatomical network information, functional connectivity, and in vivo seizure models to determine how brain networks, and their manipulation, affect seizure propagation. Guided by a known anatomical network, we applied widefield calcium imaging to determine how neural activity and seizures spread through the network regions, focusing on the primary somatosensory cortex and secondary motor cortex. We used in vivo microstimulation to induce suprathreshold excitatory activation and compared this reproducible stimulus with acute pharmacologically induced spontaneous seizure propagation. In a proof-of-concept experiment, we ablated a single node within this bilateral network and measured the effect on propagation and recruitment. Similar preliminary experiments were repeated in a chronic seizure model. The microstimulation of the somatosensory cortex propagated in a distinct pattern throughout the bilateral network with sequential reproducible node recruitment. Seizures recapitulated this same pattern, indicating a hijacking of existing pathways. Ablation of a key node in the network in the secondary motor cortex changed contralateral spread. Early chronic cobalt seizure data are presented. Here, we demonstrate a paradigm for combining widefield calcium imaging with microstimulation, cortical ablation, and seizure mapping to determine how anatomical networks inform the propagation patterns of cortical seizures. These experiments can be extended to long-term tracking of epilepsy to study epileptogenesis in other cortical networks. Our proof-of-concept findings suggest that this paradigm may be useful in the development of novel therapies for drug-resistant epilepsy patients and can be extended to the study of other disorders involving brain networks.

Application of Graph Theory in Social Network Analysis

This literature review aims to discuss the application of graph theory in analyzing social and information networks. We first introduce some key network properties, such as clustering coefficient (transitivity), centrality, and diameter, which are crucial for understanding the dynamics of information dissemination within networks. Then, we talk about, based on these properties, how graph theory can be utilized to analyze social and information networks. Lastly, we provide an overview of various fundamental social and information models including the SIR model and the Linear Threshold model. For the SIR model, we go over the definition of the SIR model, explore the mathematical methods applied to analyze the SIR model, provide one example of how the SIR model reflects the nodes status in the network, and discuss the application of the SIR model in Covid-19 epidemic. For the Linear Threshold model, we review its basic properties and explain how it can be used to calculate the maximum influence in a network

Receive-decrypt Circuit Design for Encrypted Signal Processing

In today's society where the Internet and information networks are so developed, the need for confidentiality of information transmission is more and more important, no matter whether it is in the business sector, the military, medical care, or individuals. In this paper analyses the basic working principles of four different communication systems, namely, the high-dimensional chaotic laser communication system, the chaotic synchronous confidential communication system based on the reserve pool calculation, the ultra-chaotic system based on the amnesia, and the communication system based on the Duffing vibrator array, from encrypting the signal and sending it from the transmitting end to receiving, demodulating and converting the encrypted signal from the receiving end. The differences between the different parts of each system are compared. Finally, it is found that today's communication systems have the disadvantages of poor confidentiality, weak anti-jamming, high cost, low accuracy, etc., respectively, and suggestions for modification are made. Finally, the future development of communication is looked forward to

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network

In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are crucial in treatment for many diseases. However, the toxic side effects of drug combinations are probably increased with the increase of drugs numbers, so the accurate prediction of toxic side effects of drug combinations is equally important. In this paper, we built a Metapath-based Aggregated Embedding Model on Single Drug–Side Effect Heterogeneous Information Network (MAEM-SSHIN), which extracts feature from a heterogeneous information network of single drug side effects, and a Graph Convolutional Network on Combinatorial drugs and Side effect Heterogeneous Information Network (GCN-CSHIN), which transforms the complex task of predicting multiple side effects between drug pairs into the more manageable prediction of relationships between combinatorial drugs and individual side effects. MAEM-SSHIN and GCN-CSHIN provided a united novel framework for predicting potential side effects in combinatorial drug therapies. This integration enhances prediction accuracy, efficiency, and scalability. Our experimental results demonstrate that this combined framework outperforms existing methodologies in predicting side effects, and marks a significant advancement in pharmaceutical research.

Optimization of the Effect of Network Information Flow Advertising Based on User Behavior Data Encryption and Deep Learning

Existing network information flow advertising mostly relies on user behavior data, which is easy to cause user privacy leakage and low precision of advertising delivery. This article combines user behavior data encryption and deep learning technology to propose a method for optimizing the effect of network information flow advertising. The user behavior data is encrypted using Paillier homomorphic encryption technology. Then, the stacked auto encoder (SAE) is used to extract high-order nonlinear features, and the shallow features are automatically combined based on Field-aware Factorization Machines (FFM) to construct a SAE-FFM comprehensive model. At the same time, dropout is used to reduce model overfitting, and the advertising effect is optimized through two-stage pre-training and fine-tuning. The experimental results show that when the number of impressions is 10,000, the average advertising click through rate and conversion rate of the SAE-FFM model are 4.50% and 0.59% respectively, and the information leakage rate is 0.052%. The results show that the SAE-FFM model can greatly improve the click through rate and conversion rate of advertisements, prevent privacy leakage, and help the development of the information flow advertising industry.

Mapping the community school policy network using Twitter data and social network analysis

This research sought to understand how organizations have adapted to the evolving policy ecosystem to scale and sustain a justice-orientated approach to education reform. Using descriptive social network analysis to map the policy information network that formed around Community Schools, the authors identified the influential organizations in the network and described key characteristics of its structure. They drew from Twitter data and related media articles over a five-year period to categorize the actors engaged in the Community School information network and map its geographic distribution across the United States. The findings depict a broad, multi-state network focused on promoting a proactive, positive policy agenda that differed substantially from those described in extant studies of resource networks that advance neoliberal education reforms. The authors close by discussing Twitter as a policy platform and how community-based coalitions can influence education politics and policy.

A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections

Traffic crash prediction (TCP) is a fundamental problem for intelligent transportation systems in smart cities. Improving the accuracy of traffic crash prediction is important for road safety and effective traffic management. Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. However, these works mainly focus on accidents in regions, which are typically pre-determined using a grid map. We argue that TCP for roads, especially for crashes at or near road intersections which account for more than 50% of the fatal or injury crashes based on the Federal Highway Administration, has a significant practical and research value and thus deserves more research. In this paper, we formulate TCP at Road Intersections as a classification problem and propose a three-phase data-driven deep learning model, called Road Intersection Traffic Crash Prediction (RoadInTCP), to predict traffic crashes at intersections by exploiting publicly available heterogeneous big data. In Phase I we extract discriminative latent features called topological-relational features (tr-features), of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to tr-features which capture some inherent properties of the road network, we also explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III, by exploring RNN or 1DCNN which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. We evaluate the proposed RoadInTCP model via extensive experiments on a real-world New York City traffic dataset. The experimental results show that the proposed RoadInTCP robustly outperforms existing methods.

Effectiveness of a Videoconference-Based Cognitive Behavioral Therapy Program for Patients with Schizophrenia: Pilot Randomized Controlled Trial.

Cognitive behavioral therapy for psychosis (CBTp) is not sufficiently widespread in clinical practice, although evidence has been presented. The purpose of this study was to explore whether one-on-one videoconference-based CBTp (vCBTp) is more effective than usual care (UC) alone for improving psychiatric symptoms in patients with schizophrenia attending outpatient clinics. In this exploratory randomized controlled trial, patients with schizophrenia and schizoaffective disorders who were still taking medication in an outpatient clinic were randomly assigned to either the vCBTp plus UC group (n=12) or the UC group (n=12). The vCBTp program was conducted once a week, with each session lasting for 50 minutes, for a total of 7 sessions conducted in real-time and in a one-on-one format remotely using a loaned tablet computer (iPad). The primary outcome was the Positive and Negative Syndrome Scale (PANSS) total score, which measures the difference in the mean change from baseline (week 0) to posttest (week 8). The study included 24 participants. There were no significant differences between the 2 groups at baseline. With regard to significant differences between the 2 groups in terms of the primary outcome, the mean change in the PANSS total score from baseline to week 8 in the vCBTp plus UC group was -9.5 (95% CI -12.09 to -6.91) and the mean change in the UC alone group was 6.9 (95% CI 1.54-12.30). The difference between the 2 groups was significant (P<.001). In addition, significant improvements were observed in the subscales of positive (P<.001) and negative (P=.004) symptoms and general psychopathology (P<.001). Significant differences were also observed in the secondary outcomes of the General Anxiety Disorder-7 (GAD-7; P=.04) and EQ-5D-5L (P=.005). There were no dropouts and no serious adverse events in this study. A total of 7 remote vCBTp sessions conducted in the vCBTp plus UC group could be safely administered to patients with schizophrenia. They were also observed to be effective for psychiatric symptoms, general anxiety, and quality of life. However, because of the observed worsening of scores in the UC group, caution is required in interpreting significant differences between the 2 groups. This approach is expected to improve accessibility to CBTp for outpatients with schizophrenia and social anxiety regarding transportation use and financial and physical burdens related to transportation, and to contribute to promoting CBTp acceptability by compensating for the shortage of implementers. University Hospital Medical Information Network Clinical Trials Registry UMIN000043396; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000049544. RR2-10.1136/bmjopen-2022-069734.

Electronic Patient-Reported Outcome System Implementation in Outpatient Cardiovascular Care

The integration of patient-reported outcome (PRO) assessments in cardiovascular care has encountered considerable obstacles despite their established clinical relevance. To assess the impact of a physician- and patient-friendly electronic PRO (ePRO) monitoring system on the quality of cardiovascular care in clinical practice. This open-label, multicenter, pilot randomized clinical trial was phase 2 of a multiphase study that was conducted from October 2022 to October 2023 and focused on the implementation and evaluation of an ePRO monitoring system in outpatient clinics in Japan. During phase 1 (conducted from December 2021 to March 2022), this ePRO monitoring system was developed at a single outpatient center using feedback from patients and physicians. Eligible patients were older than 18 years with a clinical diagnosis of heart failure (HF), atrial fibrillation (AF), or coronary artery disease (CAD). Participants were randomly assigned 1:1 to either the ePRO or control group. Statistical analysis was based on the intention-to-treat approach. Patients in the ePRO group were asked to complete the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12), Atrial Fibrillation Effect on Quality-of-Life Questionnaire (AFEQT), or Seattle Angina Questionnaire (SAQ) on smart tablets before their clinical examination. Results were reviewed by physicians at each outpatient visit. The control group received usual care consisting of medication management, lifestyle modification, and invasive procedures in accordance with clinical guidelines. Both groups had 5 monthly outpatient visits. Patient satisfaction, quality of information (QOI) provided by physicians, and disease knowledge were assessed using questionnaires (eg, Patient Satisfaction Questionnaire [PSQ]; score range: 5-25, with higher scores indicating greater patient satisfaction) at baseline and the fifth visit, over approximately 4 months. Of the 50 patients included, 48 (median [SD] age, 71.0 [62.3-75.0] years; 28 males [58.3%]) completed follow-up. The prevalence was 56.2% (n = 27) for HF, 75.0% (n = 36) for AF, and 10.4% (n = 5) for CAD. The distribution of PRO measures used was 21 patients (43.8%) for KCCQ-12, 24 (50.0%) for AFEQT, and 3 (6.2%) for SAQ. Compared with the control group, the ePRO group showed significant improvements in mean (SD) PSQ score (0.16 [2.06] vs 1.61 [1.75]; P = .01) and QOI score regarding treatment (-0.12 [0.53] vs 0.35 [0.71]; P = .01). The ePRO group exhibited a greater increase in the PSQ score regarding communication (-0.12 [0.53] vs 0.43 [0.90]; P = .01). In this randomized clinical trial, implementation of the ePRO monitoring system significantly enhanced patient-physician communication and the clarity of physicians' explanations about treatment. These findings suggest that the ePRO monitoring system is capable of supporting patient-centered cardiovascular care. University Hospital Medical Information Network Identifier: UMIN000049251.

Shifting from Burden Sharing to Task Sharing: Advancing Community-Initiated Care in MHPSS for Refugee Resettlement

The complexities of refugee resettlement in the U.S. require comprehensive mental health and psychosocial support (MHPSS) strategies, yet significant gaps persist due to resettlement policies prioritizing short-term self-sufficiency over long-term mental health and well-being. This study explores the shift from traditional “burden sharing” to “task sharing” models that emphasize community-initiated care (CIC). Using a two-phase qualitative method, 27 refugee leaders and bilingual service providers from 14 refugee communities, including Afghan, Bhutanese, Burmese, Congolese, Somali, and Sudanese communities, across four states participated in focus groups or key informant interviews. Thematic analysis revealed that peer support models play a critical role as bridges between cultures, service sectors, and formal and informal networks. However, peer support remains insufficient without structural reform, as refugees face barriers such as lack of professional development and power imbalances with professionals while managing their own life challenges. This study emphasizes formalizing CIC models that empower refugees to lead the sharing process in collaborative care. Intersectoral collaboration and supportive policy frameworks are necessary to sustain peer support and build long-term leadership capacity. The findings highlight the need for a system-level shift to ensure equitable responsibility for care, fostering sustainable, community-driven MHPSS solutions in refugee resettlement.