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The aim of the study to examine the level of psychological distress among nursing students volunteering in Covid-19 frontline prevention in Vietnam and related factors. Nursing students volunteering in frontline prevention presented emotional effects, including positive and negative effects on their psychological well-being. A cross-sectional study design was used and four hundred seventy-one students who volunteered for frontline prevention were randomly selected in the study using inclusion criteria. Data were collected from October to December 2021. A demographic questionnaire, the General Self-Efficacy Scale, the 6-item Kessler Psychological Distress Scale, the Brief Coping Orientation to Problems Experienced Inventory Questionnaire, and the Quality of life EQ-5D-5L were used to measure the variables. The data analysis was conducted by using descriptive statistics and linear regression. The research found that students presented a high risk of psychological distress. There was a significant correlation between problem-and emotional-coping strategies, quality of life, and psychological distress. Moreover, family support and psychological distress among nursing students had a strong relationship. Lecturers and high education institutions responsible for nursing students should pay more attention to developing psychological interventions in enhancing coping strategies and quality of life and various supports to reduce distress among nursing students fighting the epidemic.
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Objectives: The onset of COVID-19 has resulted in both morbidity and mortality. It also has a consequential impact on the Vietnamese economy. Prior studies examined the COVID-19 impact on healthcare professionals' career decisions. There remains no study examining the work conditions and career choices in a general Vietnamese population. Our study aims to identify factors associated with change in work conditions and career choices in general Vietnamese population. Method(s): An online cross-sectional study between September 2021 through to November 2021 (during the Omicron COVID-19 pandemic). Snowball sampling method was utilized in recruiting the participants. The questionnaire used in this study included the following questions: (a) Socio-demographic information;(b) impact of COVID-19 on personal habits/daily expenses;(c) Current nature of work & impact of COVID-19 on work;(d) Impact of COVID-19 on career decisions. Result(s): 650 participants were recruited, of which only 645 completed the survey. The completion rate was 99.2%. This study demonstrated the impact that COVID-19 has on finances, as only 32% of those sampled reported that they were able to pay in full. 46.6% of the respondents have had a decrease in their overall household income. With regards to their employment and work characteristics, 41.0% reported a decrease in their work satisfaction and 39.0% reported having reduced motivation for work. Females were less likely to consider transiting from their current job to another field, as compared to male participants. Respondents who were married, had a higher level of commitment to their current job, and lower inclination to transition to another field. Respondents experiencing financial difficulties were more likely to consider a transition to another field/work. Conclusion(s): This is the first study to have examined the characteristics of work/intentions with regards to career choices and transition amongst the general Vietnamese population. It is important that future financial policies take into consideration these factors.Copyright © 2023
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During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. These changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems (ESSs) to investigate the pandemic's influence on ESS development. In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on the energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City (NYC) allow us to highlight the negative impact that COVID-19 had on the NYC power sector. © 2023 IEEE.
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The requirement for quantity and quality of human resources, especially occupations in the economics field, has played a significant role in recovering and improving the COVID-19 pandemic economic situation in Vietnam. Therefore, this encouraged economics majors to attract a large number of students to enrol in 2021. This study aims to determine the factors affecting the career choices of economic students in Vietnam. The research focuses on analysing six factors to determine the relationship between variables that help explain students' compatibility and their chosen majors. A survey questionnaire using simple random sampling collected 309 data points from economics students at a private university in Vietnam. Methodologies such as Cronbach's Alpha, exploratory factor analysis, confirmatory factor analysis, regression, and structural equation modelling were employed using SPSS and Amos software to check the correlation between factors and draw conclusions about factors affecting students' career choices. The results revealed that influencers, interests, financial resources and career opportunities were critical elements that influenced students' decisions in choosing a major. Interest (to pursue passion) was considered by students in choosing a major - which could also encourage them to develop their own capabilities. Additionally, the data proved that most job selections were based on future employability;therefore, career opportunities had the most positive impact on students' decisions. The findings of this study identify determinants of students' choice in economics majors and their relationships and can improve students' awareness and future orientation before deciding to choose a major. Moreover, this study provides valuable data for universities to formulate and develop strategies to attract students, such as career consulting. © 2023 The Author(s)
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The world has witnessed the outbreak of the Covid-19 epidemic. Mainstream and social media are playing an important role in Covid-19 pandemic prevention. This research explores awareness, communication channels and effectiveness of communication in the Covid-19 pandemic in rural areas of Thua Thien Hue province, Central Vietnam. Primary information was collected from 181 respondents, who are farmers, non-farmers and students. Secondary information was collected from reports and statistical data. Television, word of mouth and local loudspeakers are the main channels of mainstream media while social media mentions the role of Facebook and Zalo to transfer Covid-19 pandemic information. Mainstream media is still the main channel of farmers and old people while non-farmers and young people tend to access information through social media. Communication has significantly contributed to improving awareness and action of rural people in the Covid-19 epidemic prevention. © 2022 World Association for Triple helix and Future strategy studies. All rights reserved.
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This article aims at evaluating the impact of debt diversification on the performance of SMEs in Vietnam. The COVID-19 pandemic has severely affected the operation results of SMEs which are looking for solutions to be able to maintain sustainable production and business activities as soon as the pandemic is under control. One of the most sought-after urgent working solutions today that SMEs are resorting to is funding from loans in various forms to avoid short-term liquidity risks. Using estimation from panel data, empirical results show that multiple lenders measured by the number of debt sources and the dispersion of debt has a negative impact on the performance of SMEs. The results imply that increase of agency costs resulting from inferior monitoring may decrease the performance of firms. Our findings contribute to the sparse literature on debt diversification of SMEs in a developing country like Vietnam. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
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The aim of the study to examine the level of psychological distress among nursing students volunteering in Covid-19 frontline prevention in Vietnam and related factors. Nursing students volunteering in frontline prevention presented emotional effects, including positive and negative effects on their psychological well-being. A cross-sectional study design was used and four hundred seventy-one students who volunteered for frontline prevention were randomly selected in the study using inclusion criteria. Data were collected from October to December 2021. A demographic questionnaire, the General Self-Efficacy Scale, the 6-item Kessler Psychological Distress Scale, the Brief Coping Orientation to Problems Experienced Inventory Questionnaire, and the Quality of life EQ-5D-5L were used to measure the variables. The data analysis was conducted by using descriptive statistics and linear regression. The research found that students presented a high risk of psychological distress. There was a significant correlation between problem-and emotional-coping strategies, quality of life, and psychological distress. Moreover, family support and psychological distress among nursing students had a strong relationship. Lecturers and high education institutions responsible for nursing students should pay more attention to developing psychological interventions in enhancing coping strategies and quality of life and various supports to reduce distress among nursing students fighting the epidemic.Copyright © 2022 Mohan R, et al.
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The COVID-19 pandemic, which affected over 400 million people worldwide and caused nearly 6 million deaths, has become a nightmare. Along with vaccination, self-testing, and physical distancing, wearing a well-fitted mask can help protect people by reducing the chance of spreading the virus. Unfortunately, researchers indicate that most people do not wear masks correctly, with their nose, mouth, or chin uncovered. This issue makes masks a useless tool against the virus. Recent studies have attempted to use deep learning technology to recognize wrong mask usage behavior. However, current solutions either tackle the mask/non-mask classification problem or require heavy computational resources that are infeasible for a computational-limited system. We focus on constructing a deep learning model that achieves high-performance results with low processing time to fill the gap in recent research. As a result, we propose a framework to identify mask behaviors in real-time benchmarked on a low-cost, credit-card-sized embedded system, Raspberry Pi 4. By leveraging transfer learning, with only 4-6 h of the training session on approximately 5,000 images, we achieve a model with accuracy ranging from 98 to 99% accuracy with the minimum of 0.1 s needed to process an image frame. Our proposed framework enables organizations and schools to implement cost-effective correct face mask usage detection on constrained devices.
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When one feels unwell, it is crucial to arrange a time as soon as possible to meet a doctor for early detection of potential health-related problems. However, a relatively large number of Vietnamese people usually avoid going to the hospital as they are afraid of long waits at such crowded places, while the current COVID-19 pandemic means being at those places poses a higher risk of contracting the disease. For simpler health problems, people would prefer a solution that, given their symptoms, provides a reliable diagnosis in a shorter time. This study presents an approach in building a deep-learning-based disease predictor of health conditions conducted from given symptoms in Vietnamese. The proposed method combines a tokenizer and bi-directional recurrent neural networks and achieved an accuracy of 98.96% (compared to a certified doctor's diagnosis) in selected test cases, demonstrating its promising capabilities in the task. The application is expected to easily be integrated into a mobile application and open the way for other deep-learning-based solutions which analyze people's symptoms to help them have their health conditions diagnosed at home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Thousands of infections, hundreds of deaths every day - these are numbers that speak the current serious status, numbers that each of us is no longer unfamiliar with in the current context, the context of the raging epidemic - Coronavirus disease epidemic. Therefore, we need solutions and technologies to fight the epidemic promptly and quickly to prevent or reduce the effect of the epidemic. Numerous studies have warned that if we contact an infected person within a distance of fewer than two meters, it can be considered a high risk of infecting Coronavirus. To detect a contact distance shorter than two meters and provides warnings to violations in monitoring systems based on a camera, we present an approach to solving two problems, including detecting objects - here are humans and calculating the distance between objects using Chessboard and bird's eye perspective. We have leveraged the pre-trained InceptionV2 model, a famous convolutional neural network for object detection, to detect people in the video. Also, we propose to use a perspective transformation algorithm for the distance calculation converting pixels from the camera perspective to a bird's eye view. Then, we choose the minimum distance from the distance in the determined field to the distance in pixels and calculate the distance violation based on the bird's eye view, with camera calibration and minimum distance selection process based on field distance. The proposed method is tested in some scenarios to provide warnings of social distancing violations. The work is expected to generate a safe area providing warnings to protect employees in administrative environments with a high risk of contacting numerous people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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When one feels unwell, it is crucial to arrange a time as soon as possible to meet a doctor for early detection of potential health-related problems. However, a relatively large number of Vietnamese people usually avoid going to the hospital as they are afraid of long waits at such crowded places, while the current COVID-19 pandemic means being at those places poses a higher risk of contracting the disease. For simpler health problems, people would prefer a solution that, given their symptoms, provides a reliable diagnosis in a shorter time. This study presents an approach in building a deep-learning-based disease predictor of health conditions conducted from given symptoms in Vietnamese. The proposed method combines a tokenizer and bi-directional recurrent neural networks and achieved an accuracy of 98.96% (compared to a certified doctor’s diagnosis) in selected test cases, demonstrating its promising capabilities in the task. The application is expected to easily be integrated into a mobile application and open the way for other deep-learning-based solutions which analyze people’s symptoms to help them have their health conditions diagnosed at home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study proposes a new model for integrating the protection motivation theory (PMT) with the technology acceptance model (TAM) to explore factors affecting students' intention to attend e-learning courses during the COVID-19 pandemic. A total of 432 valid responses to an online questionnaire were received from freshmen students studying in universities in Vietnam and Taiwan. Structural equation modeling was used to evaluate the proposed research model and test the hypotheses, and model evaluation reflected a good fit between the data and the proposed research model. Differences between perceived vulnerability, perceived severity, and intention to take e-learning courses across two countries were also established, suggesting that both the TAM and the PMT should be considered for use in studies related to technology adoption in the pandemic context. The factors influencing students' intentions to take online courses can be quite varied when different educational settings are considered;therefore, a more contextual understanding of students' e-learning intentions during pandemic times should be carefully examined. Suggestions for governments and policy makers are also proposed.
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Purpose: The study aims to seek the factors affecting perceived online learning enjoyment among university students across Viet Nam. Design/methodology/approach: Based on the concept of the technology acceptance model (TAM), this research adopts structural equation modelling (SEM) to analyse data collected from 795 undergraduate students who have gained the experience of online courses in the period of the COVID-19 pandemic. Findings: The results of this study indicate that both perceived ease of use and perceived usefulness are significant predictors of students' perceived enjoyment in online courses while perceived obstacles are not its direct antecedents. The correlation between perceived ease of use and perceived usefulness is also affirmed in this study. Besides, differences are found based on students' characteristics including gender, grade and academic major. Research limitations/implications: As online learning turns education to be learner-centred, it is crucial to have a better understanding of students' perceptions toward this advanced learning method. The insights found in this research may be of interest to educational administrators, aimed at achieving the digital transformation success in education which may adapt to the current trend of Industrial Revolution 4.0. Originality/value: According to the best of the knowledge, this research is the first to explore the effect of the group of three predictors including perceived ease of use, perceived usefulness and perceived obstacles on the students' learning enjoyment toward the online learning method, especially in the context of Viet Nam. © 2022, Emerald Publishing Limited.
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The outbreak of Coronavirus disease 2019 (COVID-19) has caused serious impacts not only on human health but also on the economies around the world. Enterprises play an important role in the development of every country but it is also one of the most affected sectors during the pandemic. Drawing on panel data of 131 enterprises listed on the Vietnamese stock exchange from 2016Q1 to 2021Q3, this study aims to investigate the impact of the COVID-19 pandemic on firm performance. Enterprises are classified into seven industries including Agriculture, Material, Industry, Real estate and Construction, Energy, Consumer, and Service. The paper also analyzes the variation of the effects among companies, focusing on differences in revenue and capital structure. The results show that the COVID-19 pandemic negatively affects business performance. In addition, the empirical findings indicate that revenue and debt decreasing can cause deterioration of firm performance during the pandemic period. The decrease in revenue has a direct impact on firm profitability. The reduction of debt levels affects the corporate leverage leading to adverse effects on firm performance. The negative effect is more pronounced for companies in some specific sectors including industry, real estate, construction, consumption, and services.
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With recent emerging events such as the pandemic of coronavirus disease (COVID) 2019, human mobility has caused significant concern in the spread of this dangerous pandemic, so mobility prediction is considered as one of the crucial factors to prevent the pandemic. Therefore, there have been many proposed and highly functional studies. Applications of social networks have stored vast data of user movements and brought a vast of interesting research on human mobility. Friendship on social networks has also revealed some effects on the movement. In this study, we have attempted to explore the influence of friendships in location-based social networks on human mobility. We conduct the movement based on the K latest check-ins of friends of the user to predict mobility. We have deployed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm (using Haversine distance) to cluster original check-in points and filtered the K latest friends’ check-ins of the user to predict the user’s next movement with the Random Forest algorithm. The prediction conducted from movement history of friends has obtained better performances compared to the prediction without considering the Friendship. The highest accuracy is 0.3176 (with a radius of 400 m and four latest check-ins of friends). Besides, we compare and evaluate the results of the proposed method with the clustered dataset with the original dataset. As observed from the experiments, clusters generated by DBSCAN with wider radii can reveal that their friends’ movements can influence users’ mobility on a location-based social network (LBSN). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The pain, namely “Covid-19 epidemic", has caused many sacrifices, loss, and loneliness. Only those who have experienced traumatic losses can fully understand the pain that is hard to erase by the epidemic. This study focuses on designing a remote medical assistance vehicle used in quarantine areas in Vietnam to support epidemic prevention with simple, cheap, easy-to-use, and multi-function criteria. The proposed system includes a 3-layer vehicle for transporting supplies controlled remotely via Radio Frequency (RF) signals to help limit cross-infection for medical staff and volunteers. The main component is the RF transceiver circuit, which transmits and receives data wirelessly over 2.4 GHZ RF using IC Nrf24l01, Nordic standard SPI interface for remote control. DC motor driver circuit BTS7960 43A controls the motor to prevent overvoltage and current drop. Moreover, the vehicle integrates an electric sprayer to support disinfecting spray a Xiaomi camera to stream video and communicate directly with patients and healthy in isolation. Ultrasonic sensors and infrared sensors aim to scan obstacles through reflected waves. The reflected signals received from the barrier objects are used as input to the microcontroller. The microcontroller is then used to determine the distance of objects around the vehicle. If an obstacle is detected, the disinfectant sprayer can stop for several seconds to ensure the safety of medical staff if there is a pass. The system has a built-in light sensor that works at night. The system is deployed at a low cost and is evaluated through some experiments. It is expected to be easy to use and is an innovative solution for hospitals. Once the outbreak is over, the product can still be used in infectious disease areas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The current study proposed and tested a moderated mediation model to reveal the effect of perceived vaccination (PV) on students' online learning intentions (SOLI) during the COVID-19 pandemic. A questionnaire was distributed to 663 full- and part-time students at Vietnamese universities, and 632 responses were analyzed. SPSS 20 software and Hayes SPSS Process Macro (model 5) were used to test five hypotheses, all of which were supported. The study found that students' online learning intentions decreased after being fully vaccinated against COVID-19 and that perceived invulnerability played a mediating role in the relationship between perceived vaccination and students' online learning intentions. The study also revealed that student age moderated a negative association between perceived vaccination and online learning intention, as this negative relationship was stronger for younger students than for older students. Theoretical and practical implications from our research contribute recommendations for governments, policymakers, and educators to consider adjusting educational management strategy, as well as adopting appropriate forms of learning in different epidemic contexts and vaccine coverage rates.
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COVID-19 temporarily hindered the development of physical stores, nevertheless, triggered the outbreak of online business. Consequently, there should be growing demands for purchasing over internet-based platforms. In order to investigate online purchase intention and online shopping behaviour, this study developed a research framework based on the analysis of 05 different variables, namely: subjective norm, attitude, behavioural control, trust, and perceived risk. A sample of 307 Vietnamese online customers has been surveyed, resulting in the findings that behavioural control and trust have a direct influence on online purchase intention, then indirectly generate online shopping behaviour. Especially, trust is considered to have the greatest effects, followed by behavioural control. From the results, implications can be established about marketing implementation in online shopping services. In other words, marketing campaigns in the COVID-19 context are aimed at highly productive purposes in order to cope with epidemic time. © 2022. Transnational Press London. All Rights Reserved.
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Automatic medical report generation is an emerging field that aims to generate medical reports based on medical images. The report writing process can be tedious for senior radiologists and challenging for junior ones. Thus it is of great importance to expedite the process. In this work, we propose an EnricheD DIsease Embedding based Transformer (Eddie-Transformer) model, which jointly performs disease detection and medical report generation. This is done by decoupling the latent visual features into semantic disease embeddings and disease states via our state-aware mechanism. Then, our model entangles the learned diseases and their states, enabling explicit and precise disease representations. Finally, the Transformer model receives the enriched disease representations to generate high-quality medical reports. Our approach shows promising results on the widely-used Open-I benchmark and COVID-19 dataset. © 2022 IEEE.