Child-friendly family reduces parenting stress in Chinese families: the mediating role of family resilience.

The increasing prevalence of parenting stress has significant implications for the psychological well-being of both parents and children. In view of this, our study sought to examine the mediating and moderating role of family resilience in the association between child-friendly family and parenting stress. Our analysis involved a sample of 316 parents who dedicated a minimum of 14 h per week to caring for their children.

COVID-19 Pandemic Risk Assessment: Systematic Review.

Background: The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.

Objective: We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.

An automatic speech analytics program for digital assessment of stress burden and psychosocial health.

The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues based on clients' speech. One hundred Cantonese-speaking family caregivers were recruited with the results suggesting that the ASAP can identify family caregivers with low or high stress burden levels with an accuracy rate of 72%.

Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data.

The COVID-19 pandemic has posed various difficulties for policymakers, such as the identification of health issues, establishment of policy priorities, formulation of regulations, and promotion of economic competitiveness. Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the policymaking process. Nevertheless, due to the abundance of data, there is a need to develop sophisticated analytical techniques and tools to efficiently extract and analyze the data.

Standardized local assortativity in networks and systemic risk in financial markets.

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely mixed networks. We build on existing literature and extend the concept of assortativity with the proposal of a standardized scale-independent local coefficient to observe the assortative characteristics of each entity in networks that would otherwise be smoothed out with a global measure.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.

Background: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic.

A moving-window bayesian network model for assessing systemic risk in financial markets.

Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of "too connected to fail" suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among stock returns to be formed with Markov properties in directed graphs.

Interviewer bias when using multiple mini-interviews in selecting student nurses in a Chinese setting.

Background: Interviewer effects may cause unfairness in assessments in multiple mini-interviews (MMIs). Due to cultural differences, the bias factors of interviewers may vary between the East and the West. MMIs are a relatively new type of assessment setting in China and few studies have been conducted to examine the interviewer effects of MMIs in this context.

Fostering integration among students with different backgrounds using an orthotic community service program.

Background: In this globalization era, institutions are developing strategies including international service-learning pedagogies to integrate global perspectives and dimensions into the learning and teaching processes to develop students' capacity in intercultural competence.

Objective: This study aimed to assess the students' intercultural learning outcome through provision of orthotic community service to the less-privileged children.

Methods: A Hong Kong-based university collaborated with 2 American universities to conduct an orthotic community service program for the children with cerebral palsy in mainland China.

Assessing government policies' impact on the COVID-19 pandemic and elderly deaths in East Asia.

This study assesses governments' long-term non-pharmaceutical interventions upon the coronavirus disease 2019 (COVID-19) pandemic in East Asia. It advances the literature towards a better understanding of when and which control measures are effective. We (1) provide time-varying case fatality ratios and focus on the elderly's mortality and case fatality ratios, (2) measure the correlations between daily new cases (daily new deaths) and each index based on multiple domestic pandemic waves and (3) examine the lead-lag relationship between daily new cases (daily new deaths) and each index via the cross-correlation functions on the pre-whitened series.

Assessing systemic risk in financial markets using dynamic topic networks.

Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time.

Learning from work-from-home issues during the COVID-19 pandemic: Balance speaks louder than words.

During the 2019 novel coronavirus disease (COVID-19) pandemic, many employees have switched to working from home. Despite the findings of previous research that working from home can improve productivity, the scale, nature, and purpose of those studies are not the same as in the current situation with the COVID-19 pandemic. We studied the effects that three stress relievers of the work-from-home environment-company support, supervisor's trust in the subordinate, and work-life balance-had on employees' psychological well-being (stress and happiness), which in turn influenced productivity and engagement in non-work-related activities during working hours.

A longitudinal examination of tablet self-management technology acceptance by patients with chronic diseases: Integrating perceived hand function, perceived visual function, and perceived home space adequacy with the TAM and TPB.

Background: Health information technologies (HITs) are increasingly being used to support the self-management of chronic diseases. However, patients' initial or continued acceptance of such technologies is not always achieved.

Objective: The aim of this study was to develop a theory-driven HIT acceptance model to examine factors affecting acceptance of HIT (measured by behavioral intention; BI) for disease self-management among patients with chronic diseases, in which we also focused on three additional, previously unexplored factors related to perceived hand function (PHF), perceived visual function (PVF), and perceived space adequacy (PSA) and a longitudinal scrutinization of changes in the effects of these factors on acceptance over time.

Stochastic actor-oriented modelling of the impact of COVID-19 on financial network evolution.

The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks.

Predicting standardized absolute returns using rolling-sample textual modelling.

Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the performances of the two datasets are compared. We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics.

Predicting the burden of family caregivers from their individual characteristics.

This study examined the association between caregivers' burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores.

Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model.

In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions.

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics.

On topological properties of COVID-19: predicting and assessing pandemic risk with network statistics.

The spread of coronavirus disease 2019 (COVID-19) has caused more than 80 million confirmed infected cases and more than 1.8 million people died as of 31 December 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected.

Pandemic risk of COVID-19 outbreak in the United States: An analysis of network connectedness with air travel data.

Objectives: The United States has become the country with the largest number of COVID-19 reported cases and deaths. This study aims to analyze the pandemic risk of COVID-19 outbreak in the US.

Methods: Time series plots of the network density, together with the daily reported confirmed COVID-19 cases and flight frequency in the five states in the US with the largest numbers of COVID-19 cases were developed to discover the trends and patterns of the pandemic connectedness of COVID-19 among the five states.

Analysis of travel restrictions for COVID-19 control in Latin America through network connectedness.

We analyze the COVID-19 pandemic development in Latin America by network analysis to demonstrate the effectiveness of air travel restriction in reducing pandemic risk and provide risk analysis for air travel reopening in Latin America. We reinforce the importance of restricting air travel before and during local transmission of COVID-19.

Visualizing COVID-19 pandemic risk through network connectedness.

With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approach - the network analysis - to provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.