The COVID-19-Related Lockdown in Qatar: Associations Among Demographics, Social Distancing, Mood Changes, and Quality of Life

Investigating fear, depressive symptoms and coping mechanisms among Egyptian nursing students amidst the covid-19 pandemic: a cross-sectional study

BACKGROUND

Amidst the ongoing COVID-19 pandemic, nursing students' contribution to combating this global health crisis is becoming more significant. However, we need a more comprehensive understanding of the psychological impact of this crisis on these students. Therefore, this study holds immense importance as it offers valuable insights into the connection between COVID-19 pandemic fear, coping strategies, and depressive symptoms among nursing students.

METHODS

A cross-sectional survey was conducted to collect and analyze data from 319 undergraduate nursing students. They participated in the study by responding to an online questionnaire. The questionnaire consisted of the Fear of the Coronavirus Questionnaire, Brief Coping Orientation to Problems Experienced, and the Beck Depression Inventory-II.

RESULTS

The study revealed that 45.5% of the participants had a severe fear of COVID-19, 21.9% showed severe depressive symptoms, and 64.6% used moderate coping mechanisms. A positive correlation (r = 0.160, p = 0.01) was found between the fear of COVID-19 and depressive symptoms. Interestingly, this correlation turned negative (r = -0.074, p = 0.01) when adaptive coping strategies such as religion, acceptance, planning, positive reframing, instrumental support, emotional support, active coping, and humor were used. However, when participants resorted to maladaptive coping strategies like venting, self-distraction, self-blame, behavioral disengagement, denial, and substance abuse, the correlation between high fear levels and depressive symptoms became positive (r = 0.352, p = 0.01).

CONCLUSION

The study demonstrated that the fear of COVID-19 and depressive symptoms among nursing students are significantly correlated. However, the nature of this correlation is influenced by the type of coping strategies employed. Adaptive coping mechanisms can mitigate the impact of fear on depressive symptoms. In contrast, maladaptive coping can exacerbate the relationship between fear and depressive symptoms. Therefore, promoting adaptive coping strategies could be a crucial approach to managing the psychological impact of the COVID-19 pandemic.

The mitigating role of physical activities on emotions is gender-specific: An experience during "lenient" COVID-19 prevention protocols

The researchers examined emotional status subsequent to changes in physical (PA) and sedentary (SA) activities during "lenient" COVID-19 prevention protocols that allowed being outdoors. Emotions, PA, and SA were collected from 272 women and 145 men in Qatar. The researchers showed a decrease (p < 0.05) in PA and an increase (p < 0.05) in SA participation during the pandemic. These alterations were different (p < 0.05) between genders and associated (p < 0.05) with emotional status during the COVID-19-induced confinement, but only (p < 0.05) in men. The "mitigating" role of PA for the adverse emotional effects of the pandemic is demonstrated, especially among men. Therefore, "lenient" regulations should be considered around the globe during future pandemics for adequate PA and emotional wellbeing. However, plans should incorporate additional tactics to PA to manage emotional status among women.

Fear, Risk Perception, and Engagement in Preventive Behaviors for COVID-19 during Nationwide Lockdown in Nepal

The world has faced huge negative effects from the COVID-19 pandemic between early 2020 and late 2021. Each country has implemented a range of preventive measures to minimize the risk during the COVID-19 pandemic. This study assessed the COVID-19-related fear, risk perception, and preventative behavior during the nationwide lockdown due to COVID-19 in Nepal. In a cross-sectional study, conducted in mid-2021 during the nationwide lockdown in Nepal, a total of 1484 individuals completed measures on fear of COVID-19, COVID-19 risk perception, and preventive behavior. A multiple linear regression analysis was used to identify factors associated with COVID-19 fear. The results revealed significant differences in the fear of COVID-19 in association with the perceived risk of COVID-19 and preventive behaviors. Age, risk perception, preventive behavior, and poor health status were significantly positively related to fear of COVID-19. Perceived risk and preventive behaviors uniquely predicted fear of COVID-19 over and above the effects of socio-demographic variables. Being female and unmarried were the significant factors associated with fear of COVID-19 among study respondents. Higher risk perception, poor health status, and being female were strong factors of increased fear of COVID-19. Targeted interventions are essential to integrate community-level mental health care for COVID-19 resilience.

A machine learning analysis of the relationship of demographics and social gathering attendance from 41 countries during pandemic

Knowing who to target with certain messages is the prerequisite of efficient public health campaigns during pandemics. Using the COVID-19 pandemic situation, we explored which facets of the society-defined by age, gender, income, and education levels-are the most likely to visit social gatherings and aggravate the spread of a disease. Analyzing the reported behavior of 87,169 individuals from 41 countries, we found that in the majority of the countries, the proportion of social gathering-goers was higher in male than female, younger than older, lower-educated than higher educated, and low-income than high-income subgroups of the populations. However, the data showed noteworthy heterogeneity between the countries warranting against generalizing from one country to another. The analysis also revealed that relative to other demographic factors, income was the strongest predictor of avoidance of social gatherings followed by age, education, and gender. Although the observed strength of these associations was relatively small, we argue that incorporating demographic-based segmentation into public health campaigns can increase the efficiency of campaigns with an important caveat: the exploration of these associations needs to be done on a country level before using the information to target populations in behavior change interventions.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.