ABSTRACT
Introduction There was a radically changed in nursing education during the nationwide lockdown due to the COVID-19 outbreaks. The transition to remote learning stressed nursing students in many countries, particularly in Vietnam. However, there is still lacking a novel study to describe the mental characteristics of nursing students in detail. Objectives To assess the mental health of nursing students, including stress, anxiety, and depression, and to identify the related factors to their mental health during the online study period because of the COVID-19 pandemic. Methods A cross-sectional survey was conducted on 540 nursing students at Dong A university using a socio-demographic questionnaire, the Depression, Anxiety, and Stress Scale - 21 Items (DASS-21). Data were analyzed by descriptive statistics and tests, including Mann-Whitney, Kruskal-Wallis, and Spearman's correlation to identify the related factors. Results In total 540 participants, nursing students reported stress (N = 120, 22.2%), anxiety (n = 195, 36.1%), and depression symptoms (n = 135, 23.1%). There was a significant relationship between age, work status, married status, number of children, stress, anxiety, and depression (P < 0.01). In addition, our study showed a negative correlation between frequency of physical activity, perceived health and stress (r = -0.117;p < 0.01, r = -0.127, p < 0.01), anxiety (r = -0.133;p < 0.01, r = -0.112, p < 0.01), depression (r = -0.134;p < 0.01, r = -0.135, p < 0.01). A significant relationship was observed between e-learning space and Internet status with mental health (p < 0.05). Especially, there was no association between average online learning time, academic workload, stress, anxiety, and depression (p > 0.05). However, the authors found a positive association between perceived level of stress related to evaluative activities and stress, anxiety, depression (r = 0.120, p < 0.01;r = 0.089, p < 0.05;r = 0.088, p < 0.05). Conclusion Nursing students suffered stress, anxiety, and depression during online learning due to the COVID-19 pandemic in the presence of some related factors. Therefore, this study may increase more attention of universities, families, and governments to reduce the stress of nursing students during distance education.
ABSTRACT
For preventing the outbreaks of Covid-19 infection in different countries, many organizations and governments have extensively studied and applied different kinds of quarantine isolation policies, medical treatments as well as organized massive/fast vaccination strategy for over-18 citizens. There are several valuable lessons have been achieved in different countries this Covid-19 battle. These studies have presented the usefulness of prompt actions in testing, isolating confirmed infectious cases from community as well as social resource planning/optimization through data-driven anticipation. In recent times, many studies have demonstrated the effectiveness of short/long-term forecasting in number of new Covid-19 cases in forms of time-series data. These predictions have directly supported to effectively optimize the available healthcare resources as well as imposing suitable policies for slowing down the Covid-19 spreads, especially in high-populated cities/regions/nations. There are several progresses of deep neural architectures, such as recurrent neural network (RNN) have demonstrated significant improvements in analyzing and learning the time-series datasets for conducting better predictions. However, most of recent RNN-based techniques are considered as unable to handle chaotic/non-smooth sequential datasets. The consecutive disturbances and lagged observations from chaotic time-series dataset like as routine Covid-19 confirmed cases have led to the low performance in temporal feature learning process through recent RNN-based models. To meet this challenge, in this paper, we proposed a novel dual attention-based sequential auto-encoding architecture, called as: DAttAE. Our proposed model supports to effectively learn and predict the new Covid-19 cases in forms of chaotic and non-smooth time series dataset. Specifically, the integration between dual self-attention mechanism in a given Bi-LSTM based auto-encoder in our proposed model supports to directly focus the model on a specific time-range sequence in order to achieve better prediction. We evaluated the performance of our proposed DAttAE model by comparing with multiple traditional and state-of-the-art deep learning-based techniques for time-series prediction task upon different real-world datasets. Experimental outputs demonstrated the effectiveness of our proposed attention-based deep neural approach in comparing with state-of-the-art RNN-based architectures for time series based Covid-19 outbreak prediction task.