Objectives The aim of this study was to evaluate psychological distress caused by the novel coronavirus disease 2019 (COVID-19) pandemic among the adult population residing in Pakistan. Materials and Methods This cross-sectional survey-based study comprised 1,000 adults residing in Pakistan. A questionnaire was formulated and circulated among adult population of Pakistan, the depression and anxiety symptoms using Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scales were assessed. Statistical Analysis Independent t-test, cross tabulation, and regression analysis were used to identify variables having impact on PHQ-9 and GAD-7 scores. A p-value of ≤ 0.05 was considered statistically significant. Results Among 1,000 participants, 573 were males and 427 were females who completed the survey. Majority were restricted to home for more than 40 days. Considerable number of participants reported depressive (540, 54%) and anxiety (480, 48%) symptoms. Gender, age, earnings, and occupation have significant relation with psychological distress, although similar was not found with education levels. Conclusion Psychological distress, a concerning yet addressable issue was found among adults arising amid COVID-19 outbreak. Currently, physical health effects of COVID-19 are being looked, while mental health effects being under-addressed. This issue should be addressed to avoid any psychological impact in future.
The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this study is to systematically review, assess, and synthesize state-of-the-art research articles that have used different ML and DL techniques to detect COVID-19 misinformation. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey was solely centered on reproducible and high-quality research. We reviewed 43 papers that fulfilled our inclusion criteria out of 260 articles found from our keyword search. We have surveyed a complete pipeline of COVID-19 misinformation detection. In particular, we have identified various COVID-19 misinformation datasets and reviewed different data processing, feature extraction, and classification techniques to detect COVID-19 misinformation. In the end, the challenges and limitations in detecting COVID-19 misinformation using ML techniques and the future research directions are discussed.
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