A cross-sectional study on the impact of the COVID-19 pandemic on psychological outcomes: Multiple indicators and multiple causes modeling.

Although the psychological impact of coronavirus disease 2019 (COVID-19) has been evaluated in the literature, further research is needed, particularly on post-traumatic stress disorder (PTSD) and psychological outcomes, is needed. This study aims to investigate the effect of the COVID-19 pandemic on psychological outcomes (depression, anxiety, and insomnia). A cross-sectional study using an online survey was conducted using the following instruments: Impact of Event Scale-Revised (IES-R), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder (GAD-7), and Insomnia Severity Index (ISI). Confirmatory factor analysis (CFA), structural equation model (SEM), multiple indicators and multiple causes (MIMIC) modeling, and differential item functioning (DIF) were performed to analyze the collected data. According to the results, participants with PTSD (n = 360) showed a higher level of depression, anxiety, and insomnia than those without PTSD (n = 639). Among the participants, 36.5% experienced moderate to severe symptoms of depression, and 32.6% had mild depressive symptoms. Moreover, 23.7% of participants experienced moderate to severe anxiety symptoms, and 33.1% had mild anxiety symptoms. In addition, 51.5% of participants experienced symptoms of insomnia. In conclusion, the PTSD caused by COVID-19 is significantly associated with depression, anxiety, and insomnia at the level of latent constructs and observed variables.

Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic.

BACKGROUND: Several instruments are currently used to assess Coronavirus Disease 2019 (COVID-19) -induced psychological distress, including the 22-item Impact of Event Scale-Revised (IES-R). The IES-R is a self-administered scale used to assess post-traumatic stress disorder (PTSD). The current study aimed to examine the construct validity of the IES-R, based on the Rasch model, with COVID-19-related data, as well as to test the multilevel construct validity of the IES-R within and among countries during the pandemic crisis. METHODS: A multi-country web-based cross-sectional survey was conducted utilizing the 22-item IES-R. A total of 1020 participants enrolled in our survey, of whom 999 were included in the analyses. Data were analyzed using Rasch modeling and multilevel confirmatory factor analysis (MCFA). RESULTS: The Rasch modeling results of the IES-R demonstrated that the IES-R is a satisfactory instrument with the five-point Likert scale, asserting that its 22 items are significant contributors to assessing PTSD as a unidimensional construct covered by the items of the IES-R. The MCFA confirmed that the 22-item IES-R, with its three factors, including intrusion, avoidance, and hyperarousal, demonstrates adequate construct validity at the within- and among-country levels. However, the results of the Akaike information criterion (AIC) model determined that the 16-item IES-R is better than the 22-item IES-R. CONCLUSION: The results suggested that the 22-item IES-R is a reliable screening instrument for measuring PTSD related to the COVID-19 pandemic, and can be utilized to provide timely psychological health support, when needed, based on the screening results.

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.

A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.