Higher education students' achievement emotions and their antecedents in e-learning amid COVID-19 pandemic: A multi-country survey.

The outbreak of the COVID-19 pandemic has had a wide range of negative consequences for higher education students. We explored the generalizability of the control-value theory of achievement emotions for e-learning, focusing on their antecedents. We involved 17019 higher education students from 13 countries, who completed an online survey during the first wave of the pandemic. A structural equation model revealed that proximal antecedents (e-learning self-efficacy, computer self-efficacy) mediated the relation between environmental antecedents (cognitive and motivational quality of the task) and positive and negative achievement emotions, with some exceptions. The model was invariant across country, area of study, and gender. The rates of achievement emotions varied according to these same factors. Beyond their theoretical relevance, these findings could be the basis for policy recommendations to support stakeholders in coping with the challenges of e-learning and the current and future sequelae of the pandemic.

Relationship Between Mental Health and Death Anxiety During COVID-19 Pandemic in Dental Staff and Students: A Cross-Sectional Study.

Objective: We aimed to determine the mental health and death anxiety among dental staff and students in school of dentistry during COVID-19 pandemic. Methods: It was a cross-sectional study among students (n = 300) and staff (n = 60) in School of dentistry in Ahvaz University of Medical Sciences during 2020. The instruments were a demographic questionnaire, Death Anxiety Scale, and Kessler Questionnaire. Data was analyze by using SPSS version 22, in all tests, the significance level was set at <0.05. Results: The mean age of dental students and personnel was 23.96 and 40.08 years, respectively. The mean scores of death anxiety were higher in dental staff (8.53) than students (6.02) and the mean scores of mental health status were higher in students (14.78) than personnel (9.18). This indicates that death anxiety was higher in Dental staff, while students were in better mental health status. The correlation coefficient between death anxiety and mental health status was 0.366 among students (p < 0.001), while it was 0.429 among dental staff (p < 0.001), showing a medium relationship between death anxiety and mental health in both groups. Conclusion: The overall findings represent a significant but contradictory relationship between mental health status and death anxiety among dental staff and students during the prevalence of COVID-19 pandemic. This suggests the impact of confounding factors in this area, which can be studied by future researchers and policy makers to design health promotion interventions.

Factor affecting the activities of daily living among aging people during the COVID-19 pandemic - a structural equation modelling.

Introduction: The activities of daily living (ADLs) are a set of basic skills necessary for self-care. The inability of elderly people to perform ADLs leads to dependence, insecure conditions, and poor quality of life. The COVID-19 pandemic has affected all aspects of the daily life of the elderly. This study aimed to determine the factors associated with ADLs among elderly people during the COVID-19 pandemic using structural equation modelling/path analysis. Material and methods: It was a descriptive-analytical study which had conducted on 487 elderly people who were selected randomly to participate in the study. Data collection tools included a demographic information questionnaire, an activities of daily living questionnaire, a knee pain and personal performance questionnaire Western Ontario and McMaster Universities Osteoarthritis (WOMAC), and the falls efficacy scale, which were completed by interview and self-report methods. SPSS-22 and AMOS software were used for data analysis. Results: Two structures of the fear of falling (FOF) and knee pain and personal performance questionnaire WOMAC had a significant role in explaining the ADL variance among the studied elderly people (p < 0.001, root mean square error of approximation = 0.063). These variables explained 64% of the ADL variance. Conclusions: The structures of this model (FOF and WOMAC) can be used as a reference framework to design effective interventions for improving ADLs among elderly people during the COVID-19 epidemic. It is also recommended that a multi-component program be provided, which includes exercise and psychological strategies for this population during the COVID-19 pandemic through online videos, distance health programs, etc.

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations.

Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

Academic student satisfaction and perceived performance in the e-learning environment during the COVID-19 pandemic: Evidence across ten countries.

The outbreak of the COVID-19 pandemic has dramatically shaped higher education and seen the distinct rise of e-learning as a compulsory element of the modern educational landscape. Accordingly, this study highlights the factors which have influenced how students perceive their academic performance during this emergency changeover to e-learning. The empirical analysis is performed on a sample of 10,092 higher education students from 10 countries across 4 continents during the pandemic's first wave through an online survey. A structural equation model revealed the quality of e-learning was mainly derived from service quality, the teacher's active role in the process of online education, and the overall system quality, while the students' digital competencies and online interactions with their colleagues and teachers were considered to be slightly less important factors. The impact of e-learning quality on the students' performance was strongly mediated by their satisfaction with e-learning. In general, the model gave quite consistent results across countries, gender, study fields, and levels of study. The findings provide a basis for policy recommendations to support decision-makers incorporate e-learning issues in the current and any new similar circumstances.

Pattern of using social networks to obtain information related to COVID-19 in Iranian students

Introduction We aimed to evaluate use of social media during the coronavirus pandemic as a source of information about COVID-19 by students. Material and methods This was a web-based study, in which the frequency and type of virtual social media used by students as a source of information about COVID-19 were evaluated by the available sampling method. The statistical population of the study consisted of 500 students of medical universities in Iran. In the first step, administrators of student groups across the country were identified and contacted and asked to assist the research team by placing a link to complete the questionnaire, after which students voluntarily completed the online questionnaire in a self-reporting manner. Results The mean age of participants was 31.29 ±10.8 years. The selection percentages based on the number of selections were: WhatsApp (35.2), Instagram (32.7), Telegram (21.2), Facebook (8.3) and other networks (2.6). Regarding the relationship between the educational level (p < 0.001) and the field of study (p < 0.01), a statistically significant difference was found for the question of which media information is more acceptable in relation to obtaining information related to COVID-19. Conclusions It was found that social media will enable these media to act as a powerful tool to change the behavior of people and promote the well-being of individuals and public health. Social media is very important in combating this contagious disease, not only to obtain information and update on it, but also to understand how it spreads, how people function and how to respond to it.

A study to design minimum data set of COVID-19 registry system.

BACKGROUND: From the beginning of the COVID-19 pandemic, the development of infrastructures to record, collect and report COVID-19 data has become a fundamental necessity in the world. The disease registry system can help build an infrastructure to collect data systematically. The study aimed to design a minimum data set for the COVID-19 registry system. METHODS: A qualitative study to design an MDS for the COVID-19 registry system was performed in five phases at Ahvaz University of Medical Sciences in Khuzestan Province in southwestern Iran, 2020-2021. In the first phase, assessing the information requirements was performed for the COVID-19 registry system. Data elements were identified in the second phase. In the third phase, the MDS was selected, and in the four phases, the COVID-19 registry system was implemented as a pilot study to test the MDS. Finally, based on the experiences gained from the COVID-19 registry system implementation, the MDS were evaluated, and corrections were made. RESULTS: MDS of the COVID-19 registry system contains eight top groups including administrative (34 data elements), disease exposure (61 data elements), medical history and physical examination (138 data elements), findings of clinical diagnostic tests (101 data elements), disease progress and outcome of treatment (55 data elements), medical diagnosis and cause of death (12 data elements), follow-up (14 data elements), and COVID-19 vaccination (19 data elements) data, respectively. CONCLUSION: Creating a standard and comprehensive MDS can help to design any national data dictionary for COVID-19 and improve the quality of COVID-19 data.

Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation.

Disease registry systems provide a strong information infrastructure for decision-making and research. The purpose of this study is to describe the implementation method and protocol of the COVID-19 registry in Khuzestan province, Iran. We established a steering committee and formulated the purposes of the registry. Then, based on reviewing the literature, and expert panels, the minimum data set, the data collection forms and the web-based software were developed. Data collection is done retrospectively through Hospital Information Systems, Medical Care Monitoring Center system (MCMC), Management of Communicable Disease Prevention and Control system (MCDPC) as well as, patients' records. For prospective data collection, the data collection forms are compiled with patients' medical records by the medical staff and are then entered into the registry system. We collect patients' administrative and demographic data, history and physical examinations, test and imaging results, disease progression, treatment, outcomes, and follow-ups of the confirmed and suspected inpatients and outpatients. From April 20 to December 5, 2020, the data of 4,812 confirmed cases and 7,113 suspected cases were collected from two COVID-19 referral hospitals. Based on our experience, recording information along with providing care for patients and putting patients' data registration in the medical staff's routine, structuring data, having a flexible technical team and rapid software development for multiple and continuous updates, automating data collection by connecting the registry to existing information systems and having different incentives, the registration process can be strengthened.