The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media—Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961–8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736–1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students.

Background The increasing physical violence against doctors in the health sector has become an alarming global problem and a key concern for the health system in Bangladesh. This study aimed to determine the prevalence and associated factors of physical violence against doctors in Bangladeshi tertiary care hospitals. Methods A cross-sectional survey was performed among 406 doctors working in tertiary care hospitals. Data were collected using a self-administered questionnaire and the binary logistic regression model was employed for predicting physical violence against doctors. Results Of the participants, 50 (12.3%) doctors reported being exposed to physical violence in 12 months prior to the survey. According to logistic regression analysis, aged less than 30 years or younger, male and never-married doctors were prone to physical violence. Similarly, doctors from public hospitals and those worked in emergency departments were at higher risk of physical violence. More than 70% of victims reported that patients’ relatives were the main perpetrators. Two-thirds of the victims referred to violence in the hospitals as a grave concern. Conclusions Physical violence against doctors is relatively common in the emergency departments and public hospitals in Bangladesh. This study found that male and younger doctors were at high risk of exposing physical violence. To prevent hospital violence, authorities must develop human resources, bolster patient protocol and offer physician training.

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Introduction:The COVID-19 is probably the most terrible pandemic in the world, because there is no approved vaccine or treatment for this rapidly spreading disease. Most of the country is locked down for reducing spreading disease. Due to lockdown situations people are afraid and confused for getting basic food available at hand. Objective:The purpose of this study is to investigate the expanded theory of planned behaviour model and adding risk perception variables for intention to reserve food during covid-19 pandemic situation among Bangladeshi internet users. Material and Methods:A total of 192 consumers living in Bangladesh actively interested in a cross-sectional web-based survey. A standardized questionnaire was generated using Google Forms and a link was shared through authors' platforms. This sample comprised 110 (57.3%) males and 82 (42.7%) females. Collected information was statistically analyzed using structural equation modelling assessment. Results:The estimation process assesses the precision and validity of the variables. Cronbach's alpha and composite reliability (Pc) was used to test the durability of the design. However, this study found Cronbach's alpha and Pc between 0.842 to 0.730 and 0.864 to 0.744. Moreover, Convergent validity is effectively identified in the findings of the analysis. Therefore, the calculation concept of the analysis has been tested successfully. Conclusion:In this analysis, we see that the stronger the risk perception of consumers, the greater the attitude of consumers to buy reserve products. This proof that high-risk awareness, in the case of a COVID-19 pandemic or others pandemic situation, can lead to the purchasing of goods that are lack of patience or common sense. The paper presented a new perspective on the negative consequences of risk perception among Bangladeshi internet users.

Background. Imposter syndrome (IS), associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction.