In this paper, we analyze and calculate the crowd density in a tourist area utilizing video surveillance dynamic information analysis and divide the crowd counting and density estimation task into three stages. In this paper, novel scale perception module and inverse scale perception module are designed to further facilitate the mining of multiscale information by the counting model; the main function of the third stage is to generate the population distribution density map, which mainly consists of three columns (...) of void convolution with different void rates and generates the final population distribution density map using the feature maps of different branch regressions. Also, the algorithm uses jump connections between the top convolution and the bottom void convolution layers to reduce the risk of network gradient disappearance and gradient explosion and optimizes the network parameters using an intermediate supervision strategy. The hierarchical density estimator uses a hierarchical strategy to mine semantic features and multiscale information in a coarse-to-fine manner, and this is used to solve the problem of scale variation and perspective distortion. Also, considering that the background noise affects the quality of the generated density map, the soft attention mechanism is integrated into the model to stretch the distance between the foreground and background to further improve the quality of the density map. Also, inspired by multitask learning, this paper embeds an auxiliary count classifier in the count model to perform the count classification auxiliary task and to increase the model’s ability to express semantic information. Numerous experimental results demonstrate the effectiveness and feasibility of the proposed algorithm in solving the problems of scale variation and perspective distortion. (shrink)

Introduction: When Coronavirus Disease (COVID-19) pandemic was declared, West Visayas State University-Himamaylan City Campus opted to adopt flexible learning to continue the learning of tertiary education students at home by using Modular Distance Learning to accommodate students living in far-flung areas without internet access. This study aimed to discover the Bachelor of Elementary Education (BEEd) students' coping strategies and insights into using modular distance learning modality. Theoretical framework: This qualitative study is anchored on constructionism. The (...) fundamental assumption of constructionism that reality is constructed which means reality is not an ontologically objective, given reality. Human invents models, concepts, and schemes to better understand or make sense with the experience and continually test and modify these constructed reality with the new experience. Humans do not construct interpretation separately, but with careful consideration of the shared practices and understanding. Methodology: Data were gathered using the qualitative phenomenological approach by conducting two semi-structured interviews with ten (10) Bachelor of Elementary Education (BEEd) university students. Participants were selected using the purposive sampling method, specifically the criterion sampling method. Thematic Analysis was done using NVIVO to analyze the data collected. Results and discussion: It was discovered that students built various coping strategies like motivating themselves to survive, taking a break after stressful days, praying to conquer difficulties, trusting God`s plan, setting up schedules to manage time effectively, and collaborating with friends and classmates to understand the complex lesson better. Appreciating the value and elements of traditional learning and realizing that everything they do is for the love is self, dreams, and family are two important insights for the students. Conclusion: Teachers may foster an environment that promotes self-motivation among students. Students may recognize their strengths, set goals, find intrinsic motivation, and develop a positive mindset to succeed academically. The school may organize activities to further enhance the spirit of collaboration, perseverance, and resilience among students. (shrink)

The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. (...) Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases. (shrink)

Currently, the coronavirus disease 2019 pandemic experienced by the international community has increased the usage frequency of borderless, highly personalized social media platforms of all age groups. Analyzing and modeling texts sent through social media online can reveal the characteristics of the psychological dynamic state and living conditions of social media users during the pandemic more extensively and comprehensively. This study selects the Sina Weibo platform, which is highly popular in China and analyzes the subjective well-being of (...) Weibo users during the COVID-19 pandemic in combination with the machine learning classification algorithm. The study first invokes the SWB classification model to classify the SWB level of original texts released by 1,322 Weibo active users during the COVID-19 pandemic and then combines the latent growth curve model and the latent growth mixture model to investigate the developmental trend and heterogeneity characteristics of the SWB of Weibo users after the COVID-19 outbreak. The results present a downward trend and then an upward trend of the SWB of Weibo users during the pandemic as a whole. There was a significant correlation between the initial state and the development rate of the SWB after the COVID-19 outbreak. LGMM results show that there were two heterogeneous classes of the SWB after the COVID-19 outbreak, and the development rate of the SWB of the two classes was significantly different. The larger class showed a slow growth, while the smaller class showed a rapid growth. Furthermore, the slope means across the two classes were significantly different. Therefore, the individuals with a higher growth rate of SWB exhibited stronger adaptability to the changes in their living environments. These results could help to formulate effective interventions on the mental health level of the public after the public health emergency outbreak. (shrink)

Artificial intelligence techniques in general and convolutional neural networks in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with (...) image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. (shrink)

In most European countries, the first wave of the COVID-19 pandemic led to the imposition of physical distancing rules, resulting in a drastic and sudden reduction of real-life social interactions. Even people not directly affected by the virus itself were impacted in their physical and/or mental health, as well as in their financial security, by governmental lockdown measures. We investigated whether the combination of these events had changed people's appraisal of social scenes by testing 241 participants recruited (...) mainly in Italy, Austria, and Germany in an online, preregistered study conducted about 50 days after the beginning of the COVID-19 outbreak in Europe. Images depicting individuals alone, in small groups, and in large groups were rated in terms of valence, arousal, and perceived physical distance. Pre-pandemic normative ratings were obtained from a validated database. Several self-report measures were also taken, and condensed into four factors through factor analysis. All images were rated as more arousing compared to the pre-pandemic period, and the greater the decrease in real-life physical interactions reported by participants, the higher the ratings of arousal. As expected, only images depicting large gatherings of people were rated less positively during, compared to before, the pandemic. These ratings of valence were, however, moderated by a factor that included participants' number of days in isolation, relationship closeness, and perceived COVID-19 threat. Higher scores on this factor were associated with more positive ratings of images of individuals alone and in small groups, suggesting an increased appreciation of safer social situations, such as intimate and small-group contacts. The same factor was inversely related to the perceived physical distance between individuals in images of small and large groups, suggesting an impact of lockdown measures and contagion-related worries on the representation of interpersonal space. These findings point to rapid and compelling psychological and social consequences of the lockdown measures imposed during the COVID-19 pandemic on the perception of social groups. Further studies should assess the long-term impact of such events as typical everyday life is restored. (shrink)

It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle. To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of (...) data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources. The conducted analysis not only does attest to the completeness of our knowledge concerning the drivers of undeclared work but also paves the way for wide usage of artificial intelligence in monitoring and confronting this detrimental practice. The study, however, exposes the necessity of having at disposal considerably larger datasets compared to those currently available if successful real-world applications of machine learning are to be achieved in this field. Alongside the apparent theoretical contribution, this paper is thus also expected to be of particular importance for policymakers, whose efforts to tackle tax evasion will have to be expedited in the period after the COVID-19 pandemic. (shrink)

COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers (...) obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803. (shrink)

The author intends to investigate the role of social distancing laws in the new normal as well as the effectiveness of forced shift to media online learning. This research indicates that student involvement had a moderating influence during the epidemic. This study is based on social learning theory, which endeavors to emulate the behavior, perceptions, and emotions of other individuals. The data were obtained from various Chinese universities. We gathered data utilizing the stratified sample approach as (...) well as Google Form. A total of 256 students enrolled in a variety of programs at Chinese universities completed a questionnaire for this investigation. The direct, mediating, and moderating effects of the variables were evaluated using partial least square structural equation modeling in this study, using the Smart-PLS software 3.0. According to the findings, forced shift to media online learning acts as a mediator between the lack of social interaction, perceived harm, perception of maintaining social distancing, and the law, and the effectiveness of media online learning has a significant effect on the effectiveness of media online learning. In addition, student engagement appears to be a moderator between the forced shift to media online learning and its effectiveness. A future study might concentrate on gaining a deeper understanding of the numerous online engagement-promoting behaviors. Teachers must go above and above to provide themes that enable pupils to connect while learning. Instructors and educational institutions will benefit from the research since it will allow them to define methods for boosting social interaction in online learning and analyze methods for enhancing the efficacy of media in online learning. (shrink)

The COVID-19 pandemic has drastically changed the lives of people all over the world. In particular, an unprecedented educational crisis has occurred due to the circumstances of physical distancing and remote learning. This article focuses specifically on the meaningful learning experiences in the everyday lives of adolescents during the pandemic. 72 meaningful learning experiences were identified from 11 participants who recorded their specific learning experiences for a week by a means of a journal recorded by (...) themselves. A content analysis was undertaken in order to identify the ecology (what, how, where, and who with) of the different learning experiences. The results show a prevalence of personal and conceptual learning, a presence of both formal and specifically informal, everyday activities among the meaningful learning experiences detected, the importance of peers, teacher and “learning experiences while alone,” and the use of digital technologies as learning resources; they also reveal the assistance of others in the learning process. The main contribution of this study illustrates how students in everyday life during pandemics are involved in a whole range of different activities both at school and at home. (shrink)

This exploratory study engages with eight case studies of music performances broadcast online to investigate the role of music in facilitating social cohesion, intercultural understanding and community resilience during a time of social distancing and concomitant heightened racial tensions. Using an online ethnographic approach and thematic analysis of video comments, the nature of audience engagement with music performances broadcast via YouTube during COVID-19 lockdown of 2020 is explored through the lens of ritual engagement with media events and (...) models of social capital. The eight case studies featured virtual choirs, orchestras and music collaborations of various genres, including classical, pop and fusion styles drawing from European, Asia Minor, South African, West African, North African, Arabic, South Asian, and East Asian cultural origins. Five overarching themes resulted from thematic analysis of video comments, includingInteraction,Unity,Resilience,Identity, andEmotion. The paper contributes important theorisation that ritual engagement and social learning fosters intercultural understanding through engaging with music both cognitively and emotionally, which can in turn shape both individual and collective identity. Online platforms provide scope for both bonding and bridging opportunities. Community resilience is supported through the sharing of knowledge, sustaining music practice during social distancing, as well as emotional support shared among audience participants, with potential wellbeing outcomes. (shrink)

Objective: Mindfulness disposition is associated with various psychological factors and prevent emotional distress in chronic diseases. In the present study, we analyzed the key role of mindfulness dispositions in protecting the individual against psychological distress consequent to COVID-19 social distancing and quarantining. Methods: An online survey was launched on March 13, 2020, with 6,412 responses by April 6, 2020. Socio-demographic information, exposure to the pandemic, and quarantining were assessed together with psychological distress and mindfulness disposition. Multivariate linear regression (...) analysis was performed to study the influence of predictive factors on psychological distress and quality of life in Italian responders during the early days of lockdown. Pearson correlations were calculated to study the relations between mindfulness and psychiatric symptoms. Results: Multivariate linear regression run on socio-demographics, COVID-19-related variables, and mindfulness disposition as moderators of overall psychological distress showed that mindfulness was the best predictor of psychological distress (β = -0.504; p =.000). High negative correlations were found between mindfulness disposition and overall Global Severity Index (r = -.637; p =.000), while moderate to high associations were found between mindfulness and all SCL-90 sub-scales. Discussion: Findings showed that high dispositional mindfulness enhances well-being and helps in dealing with stressful situations such as the COVID-19 pandemic. Mindfulness-based mental training could represent an effective intervention to stem the post-traumatic psychopathological beginnings and preventing the onset of chronic mental disorders. (shrink)

Objective:This study explored the psychological and academic effects of studying online from the home vis-à-vis host country during the coronavirus disease 2019 (COVID-19) pandemic in the experience of international students at the University of Warsaw, Poland.Methods:A total of 357 international students from 62 countries (236 in the host country and 121 in the home country) completed an online questionnaire survey 2 months after transition to online learning. We studied students' levels of loneliness, life and academic satisfaction, acculturative stress, (...) academic adjustment, performance, loyalty, and perceptions of the online learning experience.Results:The country-of-residence variable had no statistically significant effects on most psychological and academic variables. Significant effects were observed only for two academic variables. Specifically, students who returned to the home country found online communication with other students more contributing to their online learning experience and exhibited higher academic adjustment than students who remained in the host country. This suggests the positive influence of (peer and familial) support on online learning experience from the home country. Furthermore, a significant difference in experiencing acculturative stress occurred for students in quarantine/self-isolation in the host country, which expands prior literature on the disruptive effects of social distancing on students' mental health. Finally, this study confirmed the expected increased levels of loneliness among self-isolating students in both countries, hence extending prior results to the home- and host-country contexts. No relationship between self-isolation and students' life or academic satisfaction was found, which is explained by the specific nature of the learning-from-home experience. (shrink)

This study details the methodological process for creating a tool for the identification of COVID-19 potential contagion situations in sports and physical education before, during, and after practice and competition. It is a tool that implies an educational and methodological process with all the agents of the sports system. This tool identifies the large number of interactions occurring through sports action and everything that surrounds it in training, competition, and organization. The aim is to prepare contingency protocols based on (...) an exhaustive analysis, risk detection, and proposal of contingency measures trying to reduce the residual risk to a minimum. In the second part, the results of the implementation of this tool in the sports system of Galicia (Spain) are shown. The technicians have changed their perceptions about the coronavirus transmission in sports. They highlight the problem for returning to sports participation for athletes under 18 years in the pandemic context. (shrink)

Information and communication technology is included in all social strata, and its role in upbringing has intensified. The readiness of teachers as educational professionals is a crucial element for the use and implementation of ICT in the educational process. With the sudden COVID-19 pandemic, teachers were faced with designing and implementing distance learning. This research aimed to determine parents’ attitudes on using information and communication technology in distance learning during the COVID-19 pandemic; a survey was (...) conducted on 465 participants. It was found that the largest number of research participants achieve average results that are above the mean value, which means that their attitude about distance learning is generally favorable. Statistically significant differences in the use of ICT were found in individual variables of attitudes towards distance learning concerning gender, age, level of education of participants, and the class attended by the participants’ children. Qualitative analysis of open-ended questions identified the need to standardize distance learning using the same or similar digital tools to ensure equal opportunities for all students. (shrink)

This research set out to measure the impact of the lockdown condition and social distancing imposed on higher education by the Israeli government during the COVID-19 period and the shift to online learning, on students’ emotional well-being, the way they perceived their teachers’ just behavior, and faculty incivility, compared to pre-pandemic conditions. An additional aim was to explore the set of connections among these factors. The total sample included 396 undergraduate students from three academic colleges. Data were (...) gathered via three questionnaires: Positive/negative affect, Faculty Incivility, and Teacher Justice. Data were analyzed using Partial Least Squares-Structural Equation Modeling. The main finding showed that students’ negative emotions were informed by the lockdown condition. This perceived negative affect had an impact on how the participants experienced social interactions with their faculty. Those who exhibited higher levels of negative affect perceived themselves as targets of faculty incivility. The same trajectory was detected with the way students experienced their teachers’ just behavior. Students who held negative emotions, partly because of the COVID-19 restrictions, also viewed their teachers’ behavior toward them as unjust. This study stresses the role of one’s emotional condition in instigating negative interpretations of social interactions. Directions for subsequent research and practical implications for promoting students’ well-being and civil and just communications in the learning environment are discussed. (shrink)

Providing high-quality education for students with emergent proficiency in the language of instruction (referred to here as multilingual students) presents a challenge to inclusion for educational systems the world over. In Austria, a new German language support model was implemented in the school year 2018/19 which provides language support in separate classrooms up to 20 h a week. Since its implementation, the model has been strongly criticized for excluding multilingual students from the mainstream classroom, which is argued to reinforce the (...) educational disadvantages that they face. The study presented here provides unprecedented qualitative insight into how schooling for students within the so-called German language support classes (GLSC) was organized during the COVID-19 pandemic. It builds on results of a previous large-scale quantitative study (n= 3,400 teachers), which was conducted during the first lockdown (spring 2020) and indicated a high risk of exclusion for marginalized students, especially for multilingual students in GLSC. To gain deeper insights into the situation of these students during school closures, 37 teachers who work in these classes at both primary and lower-secondary schools in Vienna were interviewed, of which 18 interviews were considered for analysis. The interviews focus on the situation during the first and second school closures in the city of Vienna. A thematic analysis of the interview data reveals teachers’ perceptions of aspects which harmed or promoted inclusion for students in GLSC during these periods of school closure. Teachers’ perceptions of the most harming factors for students included strong language barriers between teachers and students, restricted access to technical equipment and supportive learning spaces, and low parental engagement. A development that promoted inclusion of these students was the option to allow them to come to school during the second school closure. Since existing studies on the schooling of students during school closures have hardly addressed the situation of students in GLSC, this study contributes to closing this research gap. (shrink)

We conducted a rapid review and quantitative summary of meta-analyses that have examined interventions which can be used by individuals during quarantine and social distancing to manage anxiety, depression, stress and subjective well-being. A literature search yielded 34 meta-analyses (total number of studies k = 1,390, n = 145,744) that were summarized. Overall, self-guided interventions showed small to medium effects in comparison to control groups. In particular, self-guided therapeutic approaches (including cognitive-behavioral, mindfulness, and acceptance-based interventions), selected positive psychology interventions, (...) and multi-component and activity-based interventions (music, physical exercise) showed promising evidence for effectiveness. Overall, self-guided interventions on average did not show the same degree of effectiveness as traditional guided individual or group therapies. There was no consistent evidence of dose effects, baseline differences and differential effectiveness of eHealth interventions. More research on the effectiveness of interventions in diverse cultural settings is needed. (shrink)

The ongoing coronavirus 2019 pandemic caused by the severe acute respiratory syndrome coronavirus 2 has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography scans of the lungs. The complexity and (...) time limitation of the reverse transcription-polymerase chain reaction swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data. (shrink)

Due to the COVID-19 pandemic, cities and states adopted social distancing, social isolation, or quarantine measurements to slow the transmission of the disease. Negative mental health outcomes including depression and anxiety have been associated with social distancing or social isolation. The purpose of the present study was to examine changes in psychological health and physical activity over an 8 week period under social distancing policies during the COVID-19 pandemic.Methods: Ninety individuals participated in this (...) study. Qualifying participants answered questions using an online survey regarding their loneliness, depressive symptoms, anxiety symptoms, mood state, and physical activity over four time points each lasting two weeks.Results: Symptoms of depression and state anxiety were increased in the population when compared to nationwide statistics from before the COVID-19 pandemic. Time point 2, ~1 month into social isolation, showed the most significant effects on mental health. During this time point, 100% of the participants showed symptoms of depression. There were no significant changes in physical activity over the 8 weeks. Loneliness, depressive symptoms, fatigue, and mood state were negatively associated with participation in physical activity. Vigor and state anxiety were associated with participation in physical activity.Conclusion: Social isolation and social distancing practices have had a negative effect on depression, anxiety, and mood over time. It appeared that depressive symptoms and total mood disturbance was elevated during time point two. Depressive symptoms were much higher than average compared to previous epidemiological data. Physical activity amount did not change over time but was associated with poor mental health. (shrink)

At the beginning of the pandemic, many music ensembles had to stop their activities due to the confinement. While some found creative ways to start making music again with the help of technologies, the transition from “real” rehearsals to “online” rehearsals was challenging, especially among older amateur musicians. The aim of this case study was to examine the effects of this transition on three community band conductors and three older amateur musicians. Specific objectives were to explore intergenerational relationships to support (...) online group music-making; digital literacy and access in later life; and online music-making in a COVID-19 context. Semi-structured interviews were conducted and theoretical thematic analysis was undertaken. Results were analyzed from the conductors’ and older musicians’ perspectives, and common trends were combined to facilitate interpretation. The first theme showed that being part of an intergenerational ensemble contributed positively to the learning experience online. The second theme demonstrated that because both conductors and musicians were new to the online rehearsals, it contributed to attenuate the age-related digital divide that may have been observed in other studies. Regarding access in later-life, older musicians reported benefits associated with rehearsing online, specifically in terms of distance/commute, time, energy, and cost. However, for those who did not already have internet and electronic devices, the cost of acquiring all the necessary equipment to make music online could have been too high. Finally, the third theme revealed that musicians appreciated the opportunity to make music online and indicated that it was definitely better than having nothing, especially for its social aspects. In conclusion, while participants noted several challenges associated with online music-making, they were also appreciative of the opportunity to continue making music at a time when in-person rehearsals were not possible. Pedagogical implications are discussed, specifically the importance of the support network, of meeting people where they are, of learning to adapt, and of collaborative teaching. (shrink)

This paper employs a phenomenological approach to examine the centrality of embodied habit in both the proliferation and the transmission of COVID-19. The analysis focuses not only on the difficulty of amending embodied habits but on the question of the ethics of social distancing and the role of human agency in the amendment of such habits. To this effect, the relation between passivity and activity in the uptake of habit is emphasized and the active and agential aspects of (...) embodied habit are highlighted. In contrast to the predominance of more negative accounts of social distancing, the paper considers the kind of ethical reorientation that is required to amend embodied and social habit in the face of the mutual vulnerability evoked by COVID-19. This analysis is distinguished from more far-reaching government measures in relation to COVID-19; instead, it offers a consideration of the kinds of responsibility towards one another at the level of everyday life. (shrink)

The COVID-19 pandemic was a global health crisis that required US residents to understand the phenomenon, interpret the cues, and make sense within their environment. Therefore, how the communication of COVID-19 was framed to stakeholders during the early stages of the pandemic became important to guide them through specific actions in their state and subsequently with the sensemaking process. The present study examines which frames were emphasized in the states' press releases on policies and other COVID information (...) to influence stakeholders on what to focus on to help with sensemaking during the crisis. We conducted content analysis on 602 press releases from 50 US states using an unsupervised machine learning approach called Latent Dirichlet Allocation (LDA). The results show that health communication using press releases to help the public make sense of the crisis were framed to include health frames as well as economic frames. Health communication messages are typically framed with health and safety measures; however, this study shows that economic frames were emphasized more than public health frames in the government's health communication for COVID-19, which forced both large and small businesses to engage in specific socially responsible activities that were previously voluntary to support public health safety. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron (...) neural network, decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

Background: Burnout is a stress-induced syndrome considered to be closely related to work. Although social support could relief burnout syndrome, its effect on learning burnout in medical students remains unclear. The objectives of the study are to evaluate the association between learning burnout and social support in Chinese medical students.Methods: A cross-sectional online survey was distributed to students who participated in online learning in a medical college in Wuhan during the COVID-19 epidemic. We used (...) the Lian version of the Maslach Burnout Inventory to assess learning burnout and the Social Support Rating Scale to assess social support. Chi-square tests were used to analyze factors associated with burnout. Independent t-test and multiple logistic regression were explored to analyze the relationship between social support and burnout.Results: A total of 684 students have completed the survey, of which 315 met standard criteria for learning burnout. Multiple logistic regression analysis has revealed that seniors, low family income and low social support were significant predictors of learning burnout. After adjusting for the grade and family income, there was a significant and relevant association between social support and learning burnout.Conclusions: Learning burnout was highly prevalent in medical students at our college. Senior students and low family income might be risk factors for learning burnout. Social support, especially subjective support and utilization of support might play a protective role in reducing the risk of learning burnout. (shrink)

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within “e-learning in higher education.” E-learning as a concept has been widely used in the academic and professional communities and has been approved as an educational approach during COVID-19. This article starts with a literature review of e-learning. Diverse subjects have appeared on the topic of e-learning, which is indicative of the dynamic and multidisciplinary nature of (...) the field. These include analyses of the most influential authors, of models and networks for bibliometric analysis, and progress towards the current research within the most critical areas. A bibliometric review analyzes data of 602 studies published in the Web of Science database to fully understand this field. The data were examined using VOSviewer, CiteSpace, and KnowledgeMatrix Plus to extract networks and bibliometric indicators about keywords, authors, organizations, and countries. The study concluded with several results within higher education. Many converging words or sub-fields of e-learning in higher education included distance learning, distance learning, interactive learning, online learning, virtual learning, computer-based learning, digital learning, and blended learning. This research is mainly focused on pedagogical techniques, particularly e-learning and collaborative learning, but these are not the only trends developing in this area. The sub-fields of artificial intelligence, machine learning, and deep learning constitute new research directions for e-learning in light of COVID-19 and are suggestive of new approaches for further analysis. (shrink)

COVID-19 added a new dimension to the relationship between school professionals and students’ families: a virtual one. To explore this shift and the associated challenges, we performed a bibliometric analysis of research literature published on the topic to the end of 2021. Our guiding question was: what kind of themes are emerging in literature on the school-family relationship in association with COVID-19? Our search of Scopus, Web of Sciences and ERIC retrieved 286 articles. Using VOSviewer, we conducted a (...) bibliometric analysis based on article keywords to map the field, producing a visual representation of networks of themes. Four clusters emerged: “educational players”, “distance learning”, “partnership” and “social context”. Unsurprisingly, the analysis suggests a shift in the roles of parents and teachers, with the digital sphere becoming central to their relationship. Beyond this, the crisis has cast light on both existing problems and the potential of a partnership based on a genuine sharing of responsibility. (shrink)

The transition to distance education has led to deterioration in the health of teachers and students. The purpose of the study was to identify controlled factors of the educational environment, the impact of which in an emergency situation due to a pandemic on infectious disease can be influenced by the administration of general secondary education institutions. Material and methods: 339 teachers and 828 parents of general secondary schools of Mykolaiv region (Ukraine) took part in research from May to June 2021. (...) The research tool was a self-administered survey questionnaire.The study found that social networks were the leading source of information on the COVID-19 pandemic situation for educators. The vast majority of educators indicated that they follow certain rules of conduct in order not to get sick. Most respondents admitted that they experienced weakness, mood swings, depressed thoughts, anxiety and tension due to the situation with the COVID-19 pandemic. Mostly more fatigue, hypodynamics and impaired vision were considered by teachers as manifestations of deteriorating physical condition. Manifestations of psychological deterioration, according to teachers, were greater aggression, agitation, signs of depression and emotional instability. The key factors in preventing the deterioration of teachers’ health in the event of an infectious disease pandemic, which can be influenced by the administration of general secondary education institutions, include: reducing the workload of teachers during distance education; conducting professionally oriented education of teachers in the field of health technologies; increasing the level of information competence of teachers; improving the technological base of schools for distance learning. (shrink)

We explore multi-scale relations in artificial intelligence (AI) use in order to identify difficulties with coordinating relations between users, machine learning (ML) processes, and “sociobuilt contexts”—specifically in terms of their applications to medical technologies and decisions. We begin by analyzing a recent COVID-19 machine learning case study in order to present the difficulty of traversing the detailed causal topography of “sociobuilt contexts.” We propose that the adequate representation of the interactions between social and built processes that (...) occur on many scales ought to drive interdisciplinary approaches for ML modification. Next, we describe ML algorithm development as a process that is partly dependent on methodological stabilization for reliability and partly on coordinating relations. In the coordination between user, ML process, and sociobuilt contexts, we propose that new methods can be explored that promote the inclusion of patients and communities for the purpose of cross-checking portions of the ML process. Finally, we suggest that the advantages of responsible innovation emerge through the iterative entanglement of ethical, methodological, and ontological considerations within the broader conceptual infrastructure of epistemic responsibility. (shrink)

The current study describes the implementation of an online Future Problem Solving program in the field of Health education and set out to explore its contribution to students' eHealth Literacy identity, by using two levels of teacher guidance: minimal vs. frequent. FPS was employed in two groups of Health students. In the research group, frequent weekly guidance was provided to the students centered on the enhancement of eHealth Literacy skills, whereas in the control group minimal guidance was offered by the (...) lecturer. Data for the analysis were gathered from 113 Israeli undergraduate students of a Management of Health Service Organizations program, of whom 62 comprised the research group. Data were gathered twice, pre- and post-program implementation from both groups. Findings showed significant differences between the tests only for the research group, with increased levels of eHealth Literacy skills detected between the tests. The perception of the FPS program as meaningful contributed to students' perceived eHealth Literacy skills only in the research group whereas non-significant results were shown for the control group. This study mainly shows that the enhancement of skills in online educational environments requires frequent and personalized guidance. Faculty must recognize the role of the instructor as a facilitator of learning and design successful scaffolding strategies to nurture students' lifelong learning skills during distance learning. (shrink)

In 2020, the COVID-19 pandemic forced many nations to shut-down schools and universities, catapulting teachers and students into a new, challenging situation of 100% distance learning. To explore how the shift to full distance learning represented a break with previous teaching, we asked Austrian students (n = 874, 65% female, 34% male) which digital media they used before and during the first Corona lockdown, as well as which tools they wanted to use in the future. Students additionally (...) reported on their attitudes and experiences with online learning. Results showed that students used certain tools, such as video, audio, e-assessments, and web conferencing systems, much more often during lockdown than they had before. Their use of classic digital media, such as e-mail, social communication tools, such as chat or online forums, and other interactive tools, such as wikis or educational games, hardly changed at all. Their attitudes toward multimedia learning were positively related to their media use. In their open responses (n = 137), students identified advantages of online learning (flexibility and self-directed learning), as well as disadvantages (limited social interaction) and challenges (motivation and self-discipline). As a group, they also expressed a clear preference for a balanced combination of online- and offline teaching in the future. However, individual students did prefer fully online or offline learning modes, depending on their personal circumstances and educational goals. We view this as a call to researchers and educators alike to explore ways in which the advantages of online and face-to-face learning can best be combined to meet the changed needs and expectations of organizations, students, and teachers in a future “after Corona.”. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the (...) current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the (...) predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic. (shrink)

The COVID-19 pandemic shocked the global economy, with numerous companies suffering losses and shutting down. However, some companies proved to be resilient, being able to sustain their economic performance despite the pandemic. The study aims to explain the sustainable economic performance of companies during the COVID-19 pandemic. The relationships between empowering leadership, innovative work behavior, organizational readiness to change, and sustainable economic performance were assessed. The data were collected via an online questionnaire from January 2021 to March 2021, (...) during the height of the COVID-19 pandemic in Russia. The respondents were Russian companies’ employees holding management positions, competent to objectively assess organizational circumstances during the COVID-19 pandemic. A sample of 337 was used in the analysis. Confirmatory factor analysis (CFA) with maximum likelihood estimation was conducted using SPSS AMOS. The structural model was tested with standardized parameter estimates, standard errors, and p-values calculated. The findings of the study suggest that innovative work behavior and organizational readiness to learn have a direct influence on sustainable economic performance. The findings also suggest that empowering leadership impacts innovative work behavior but not sustainable economic performance. The mediation analysis indicates that innovative work behavior is a mediator between empowering leadership and sustainable economic performance, whereas organizational readiness to learn is not a moderator. The study adds to the leadership and sustainability body of knowledge and contributes to the research on the COVID-19 pandemic in the organizational context. (shrink)

Previous research in the social sciences has shown that haptic interaction rituals are critical for maintaining social relationships. However, during the coronavirus pandemic, ‘social distancing’ was encouraged in order to avoid the spread of disease. Drawing on data from self-ethnography as well as publicly available resources, in this study we explore some new, locally negotiated haptic trajectories to accomplish interaction rituals in the time of coronavirus. First, we present self-ethnographic observations of distancing in face-to-face encounters from our (...) everyday lives. Second, utilizing methods of microanalysis of naturally occurring interaction, we investigate video recordings of the embodied negotiation of space and touch among politicians. We analyze three different ways in which politicians negotiate transitional moves in this haptic ritual when one party initiates a handshake: repairing, declining and apologizing. Our analysis shows that politicians adapt their entire bodies in conjunction with talk, gestures and laughter not only to accomplish the greeting but also to remedy the potentially face-threatening situation of not getting the greeting right. This research has implications for better understanding the spontaneous ability of human beings to invent new ways of engaging with each other. Moreover, it adds to our knowledge of how the materiality of human bodies can impact forms of sociality. (shrink)

Connecting with peers online to overcome social isolation has become particularly important during the pandemic-related school closures across many countries. In the context of contact restrictions, feelings of isolation and loneliness are more prevalent and the regulation of these negative emotions to maintain a positive well-being challenges adolescents. This is especially the case for those individuals who might have a high need to belong and difficulties in emotional competences. The difficult social situation during contact restrictions, more time for (...) online communication and maladaptive emotion regulation might lead to aggressive communication patterns in the form of cyberbullying perpetration. In an online study with N = 205 adolescents aged 14–19, we assessed the frequency of online and offline contacts, need to belong, emotion regulation problems, feelings of loneliness, and cyberbullying perpetration as predictors of adolescents’ well-being. In particular, we explored whether cyberbullying perpetration might function as a maladaptive strategy to deal with feelings of loneliness and therefore predicts well-being. This effect was expected to be stronger for those with a higher need to belong and with higher emotion regulation problems. Results of a hierarchical regression analysis revealed that well-being was significantly predicted by less emotion regulation difficulties, less feeling isolated and more cyberbullying perpetration. We also tested whether the need to belong or emotion regulation problems moderated the association between cyberbullying and well-being. While the results for emotion regulation problems were not significant, the moderation effect for the need to belong was significant: For students with a high need to belong, well-being was more strongly related to cyberbullying perpetration than for students with a medium need to belong. For students with a low need to belong, cyberbullying was not significantly associated with well-being. That cyberbullying perpetration predicted well-being positively is rather surprising in the light of previous research showing negative psychosocial outcomes also for cyberbullying perpetrators. The moderation analysis provides a hint at underlying processes: In times of distance learning and contact restrictions, cyberbullying may be a way of coming into contact with others and to regulate loneliness maladaptively. (shrink)

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well (...) as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing. (shrink)

Insular populations have traditionally drawn a lot of attention from epidemiologists as they provide important insights regarding transmission of infectious diseases and propagation of epidemics. There are numerous historical instances where isolated populations showed high morbidity once a new virus entered the population. Building upon that and recent findings that the activation of the behavioral immune system depends both upon one’s vulnerability and environmental context, we predicted that, during the COVID-19 pandemic, place of residence explains a significant proportion of (...) variance in preferred interpersonal distances, animosity toward strangers, and willingness to punish those who do not adhere to COVID-19 preventive measures. With 48 populated islands, Croatia provides a fruitful testing ground for this prediction. We also opted to explore relations among BIS-related variables and social cognitions in a more natural context than has previously been done. The study was conducted online, on Croatian residents, during April and May 2020. As expected, the BIS variables contributed significantly to preferred interpersonal distances, negative emotions toward strangers, and willingness to punish those who do not adhere to COVID-19 preventive measures. Furthermore, our results showed that geographical location explained a significant amount of variance in preferred social distances and negative emotions toward foreigners. As Croatian islands are extremely frequent travel destinations, these differences between mainlanders and islanders cannot be explained by the lack of exposure to foreigners. Additionally, we found that scores on preferred interpersonal distances, pathogen disgust, and germ aversion were significantly higher compared to those obtained in Croatian samples before the COVID-19 pandemic. Furthermore, men scored higher in perceived infectability than before the COVID-19 pandemic, and women did not, which reflects the objectively higher risk of SARS-CoV-2 for men than for women. Taken together, our results support the notion that BIS is a highly adaptive and context-dependent response system, likely more reactive in more susceptible individuals. (shrink)

Social distancing during the COVID-19 pandemic has presented millions of people with extraordinary challenges that are associated with significant amounts of stress. Emotion regulation is crucial during this crisis as people seek to mitigate the stress and uncertainty of the present moment. In this study, we surveyed a nationally representative sample of 297 adults from the United States on their levels of perceived stress related to the COVID-19 pandemic as well as their level of engagement of different (...) emotion regulation strategies during the pandemic. We performed multiple linear regression analyses to assess which regulation strategies were associated with individual differences in perceived stress. Among all emotion regulation strategies, psychological distancing, which involves thinking about stressful circumstances in an objective, impartial way, was uniquely associated with reductions in perceived stress due to COVID-19 across individuals. This effect was not moderated by age, gender, socioeconomic status, race/ethnicity, or trait-related difficulty in regulating emotion. Conversely, situation modification was associated with significantly greater perceived stress overall. These results suggest the broad applicability and utility of psychological distancing during pandemic-related social distancing as part of an adaptive emotion regulation toolkit and motivate the investigation of interventions involving psychological distancing in this context. (shrink)

This study investigated the relationship between cultural value orientations and country-specific changes in mobility during the Covid-19 pandemic. The aim was to understand how cultural values relate to mobility behavior during the initial stages of the pandemic. The aggregated data include Schwartz's cultural orientations, Gross Domestic Product (GDP) per capita, number of Covid-19 cases per million, and mobility change during the Covid-19 pandemic (Google Mobility Reports; percentage decrease in retail and recreation mobility, transit station mobility, workplace mobility (...) and percentage mobility increase in residential areas). Regression analyses showed that, after controlling for economy and severity of disease, hierarchy was the primary factor reducing mobility, such as staying at home, and mobility in public spaces, such as avoiding retail and recreation sites (marginally significant). The results are discussed in the light of previous literature and the implications for social distancing measures. (shrink)

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or (...) state secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about (...) the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus. (shrink)

The coronavirus disease 2019 pandemic has severely affected workers, workplaces, and working practices. In the higher education setting, universities have shifted to distance learning, resulting in profound changes in academics' work. In this study, we aimed to describe academics' job demands and resources related to changes in working conditions during the pandemic, and to examine how these changes have affected the perceived occupational wellbeing of academics. Additionally, we aimed to investigate academics' expectations and concerns for future academic working practices (...) following the pandemic. The data were collected through semi-structured interviews with 26 academics working at various universities in Sweden. A content analysis was used to identify the key themes from the transcribed interviews. The results indicated that academics experienced a lack of face-to-face communication, absence of an academic environment, work overload, and work-home interference as demanding during the pandemic. In relation to resources, online communication options, appropriate working conditions, organizational-social support, and individual factors were perceived as important. Most respondents perceived negative occupational wellbeing outcomes. However, academics who had the appropriate resources were less likely to be affected by job demands. Academics' expectations for future academic work included continuation of working online, flexibility in the choice of workspace, and strengthened digital capacity. Their concerns were related to a lack of face-to-face interaction, management actions and economic implications, and pure digital education. This paper contributes to the literature by illustrating the complexity and diversity of experiences and preferences among academics that are important for universities to consider when organizing and managing future academic work. (shrink)

During the 2020 COVID-19 epidemic a variety of social activities migrated online, including religious ceremonies and rituals. One such instance is the case of Santo Daime, a Brazilian rainforest religion that utilizes the hallucinogenic brew ayahuasca in its rituals. During the pandemic, multiple Santo Daime rituals involving the consumption of ayahuasca took place online, mediated through Zoom and other online platforms. The phenomenon is notable since the effects of hallucinogens are defined by context (set and setting) and Santo (...) Daime rituals are habitually governed by punctilious regulations aimed at directing the experience of participants. The abrupt move to online space thus augurs significant implications in the context of hallucinogenic rituals. This paper looks at this development and its repercussions for entheogenic rituals, as it asks how do psychedelic rituals change when they move online? Building on the author’s previous work on set and setting in the Santo Daime religion, the paper introduces accounts from 12 semi-structured interviews with daimistas participating in online daime rituals, approached through the prism of set and setting, and the study of online religiosity. The analysis points at several key dynamics emerging in the context of virtual rituals. The migration online allowed for the continuation of Santo Daime entheogenic rituals at a time of social distancing, fostered a sense of global brotherhood and opened new possibility for religious participation and learning. Concurrently, online ritual produced an impoverished ritual experience and novel types of challenges including a higher potential for distractions, technical difficulties, and low sensory fidelity. Other novel challenges included social anxiety and an in-built tension between the social and spiritual dimensions of ritual. Finally, some participants were concerned by the cultural context of online rituals: technological mediation, consumerism, commodification, and digital divide. The limitations of digital technology appear amplified by the highly immersive, body oriented, experientially intensified context of the psychedelic experience. This paper contributes to the literature on the extra-pharmacological factors shaping experiences with psychedelics, as well as to the literature on the consequences of the adoption of digital media technologies during the COVID-19 pandemic. (shrink)

The COVID-19 pandemic and its associated stressors have impacted the daily lives and sleeping patterns of many individuals, including university students. Dreams may provide insight into how the mind processes changing realities; dreams not only allow consolidation of new information, but may give the opportunity to creatively “play out” low-risk, hypothetical threat simulations. While there are studies that analyze dreams in high-stress situations, little is known of how the COVID-19 pandemic has impacted dreams of university students. The aim (...) of this study was to explore how the dream content of students was affected during the university COVID-19 lockdown period (March–July, 2020). Using online survey methods, we analyzed dream recall content (n= 71) using the Hall-Van de Castle dream coding system and Fisher's exact tests for sex comparisons. Preliminary results indicate that female students experienced more nightmares as compared to male students. Dream analysis found that, relative to normative American College Student (ACS) samples generated pre-COVID-19, women were more likely to experience aggressive interactions in their dream content, including increased physical aggression. Results indicate that university students did experience changes in dream content due to the pandemic lockdown period, with women disproportionally affected. These findings can aid universities in developing support programs for students by bringing forth an understanding of students' concerns and anxieties as they process the “new normal” of social distancing. (shrink)

COVID-19 has made it difficult to adopt traditional face-to-face psychological intervention under this situation because of the blocked down and social distancing, which brings big psychological crisis to the public among the global. To explore the emotional change of the public in China at the outburst of the pandemic at different phases, to establish an online working platform and create a new model of an online intervention to hold public emotions under pandemic, and test its effectiveness, so to (...) give advisement for government emergency management system. We established an online organization to work for this program ad innovated a model of online group counseling with online emotional support accompany group right after the outburst of a pandemic. We analyzed 53 OESAGs from February 10 to April 9, including 555 application forms, 253 feedback from members, and 139 feedback from group leaders by using NVivo and SPSS to explore the evolution and characteristics of public emotion during COVID-19 and the effectiveness of OESAG. Our results showed that the emotional changes of members ranged from shock to depression to positive. The public's emotions swiftly changed from stress, anxiety, and isolation, to the hope of returning to work or finding a job during the pandemic with the help of OESAG. OESAG has effectively regulated the negative emotions of members by conducting psychological crisis intervention to provide members a space to communicate with each other, especially the female and frontline staff. Policy makers can set up an online systematic psychological crisis intervention system as soon as possible to make up for the lack of psychological assistance in the emergency management system. (shrink)

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human (...) brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification. (shrink)

This paper examines the trust relations involved in Israel’s COVID-19 vaccination campaign, focusing on vaccine hesitancy and the concept of ‘trust’. The first section offers a conceptual analysis of ‘trust’. Instead of analyzing trust in the vaccination campaign as a whole, a few objects of trust are identified and examined. In section two, the Israeli vaccination campaign is presented, and the focus is placed on vaccine hesitancy. In section three, different trust relations are examined: public trust in the Israeli (...) government and health institutions, interpersonal trust in healthcare professionals and experts, trust in the pharmaceutical companies that make the COVID-19 vaccine, the US FDA, and trust in the new vaccine and the new technology. Through this complexity of trust relations, I argue that it is impossible to completely separate the trust that the vaccine is safe and effective from social aspects of mistrust. Additionally, practices of silencing and censoring the concerns of vaccine hesitaters – both experts and among the public, are pointed out. I contend that these cases further minimize vaccine hesitaters' trust in vaccine-related entities. In contrast, in section four, I suggest the ‘trust-based approach’: since vaccine hesitancy is not solely the result of knowledge deficiency but also a lack of trust relations, any campaign that addresses vaccine hesitancy should also focus on trust. The advantages of this approach are spelled out. For governments, a discussion based on trust is, ultimately, the best democratic way to encourage hesitaters to take the plunge and get vaccinated. (shrink)

The article entitled “Post-COVID-19: Education and Thai Society in Digital Era” has two objectives: 1) to study digital technology 2) to study the living life in Thailand in the digital era after COVID-19 pandemics. According to the study, it was found that the new digitized service is a service process on digital platforms such as ordering food, hailing a taxi, and online trading. It is a service called via smartphone. The information is used digitally. Public relations, digital marketing, (...) and living in cyberspace play an increasingly digital role. People have communication skills, use Line, Facebook, social media, have tools to search for knowledge, learn and improve themselves. People in the new era of life have a new way of life therefore there is social life in the cyber world. There should be ethics, attitudes, values, and click-on images that are linked to digital use. For a good time life, It is important to have digital intelligence (Digital Quotient) to live after post-COVID19, the digital era. (shrink)

Nation-wide community quarantines and social distancing are part of the new normal because of the global COVID-19 pandemic. Since extensive and prolonged lockdowns are relatively novel experiences, not much is known about the well-being of individuals in such extreme situations. This research effort investigated the relationship between well-being elements and resiliency of 533 Filipino adults who were placed under the nationwide enhanced community quarantine during the COVID-19 pandemic. Participants comprised of 376 females and 157 males. The median (...) and mode ages of the participants is 23 years, while 25 is the mean age. PERMA Profiler was used to measure participants’ well-being elements, while Connor-Davidson Resiliency Scale-10 was used to measure their resiliency. Collected data were analyzed using the regression model and necessary condition analysis. This study corroborated that all the five pillars of well-being are significant positive correlates of resiliency in quarantined adults. The results shown accomplishment positively predicts resiliency, while negative emotions negatively predict resiliency. Lastly, the five pillars of well-being are necessary-but-not-sufficient conditions of resiliency. Our results cast a new light on well-being elements as constraints rather than enablers of resiliency. This novel result shows that optimum resiliency is only possible when all the five pillars of well-being are taken care of and when a person is at least minimally contented with their physical health. The present findings underscore the importance of a holistic as against an atomistic approach to maintaining good mental health, which suggests that deficiencies in certain areas of well-being may not be fully addressed by overcompensating on other areas, as all five pillars of well-being are necessary-but-not-sufficient conditions of resiliency. The study ends with the recommendation for the use of necessary condition analysis to study both classical and novel psychological research problems. (shrink)