Evaluation of preventive, supportive and awareness building measures among international students in China in response to COVID-19: a structural equation modeling approach.

BACKGROUND: Education institutions promptly implemented a set of steps to prevent the spread of COVID-19 among international Chinese students, such as restrictive physical exercise, mask wear, daily health reporting, etc. Success of such behavioral change campaigns largely depends on awareness building, satisfaction and trust on the authorities. The purpose of this current study is to assess the preventive, supportive and awareness-building steps taken during the COVID-19 pandemic for international students in China, that will be useful for planning such a behavioral change campaign in the potential pandemic situation in other parts of the world. METHODS: We conducted an online-based e-questionnaire survey among 467 international students in China through WeChat. The data collection duration was from February 20, 2020 to March 10, 2020 and we focused on their level of awareness, satisfaction, and trust in authorities regarding pandemic measures. Simple bivariate statistics was used to describe the background characteristics of the respondents along with adoption of the partial least squares-structural equation modeling (PLS-SEM) as the final model to demonstrate the relationship between the variables. RESULTS: In our study, the leading group of the respondents were within 31 to 35 years' age group (39.82%), male (61.88%), living single (58.24%) and doctoral level students (39.8%). The preventive and supportive measures taken by students and/or provided by the respective institution or authorities were positively related to students' satisfaction and had an acceptable strength (ß = 0.611, t = 9.679, p < 0.001). The trust gained in authorities also showed an acceptable strength (ß = 0.381, t = 5.653, p < 0.001) with a positive direction. Again, the personnel awareness building related to both students' satisfaction (ß = 0.295, t = 2.719, p < 0.001) and trust gain (ß = 0.131, t = 1.986, p < 0.05) in authorities had a positive and acceptable intensity. Therefore, our study clearly demonstrates the great impact of preventive and supportive measures in the development of students' satisfaction (R2 = 0.507 indicating moderate relationship). The satisfied students possessed a strong influence which eventually helped in building sufficient trust on their institutions (R2 = 0.797 indicating above substantial relationship). CONCLUSIONS: The worldwide student group is one of the most affected and vulnerable communities in this situation. So, there is a profound ground of research on how different states or authorities handle such situation. In this study, we have depicted the types and magnitude of care taken by Chinese government and educational institutions towards international students to relieve the panic of pandemic situation. Further research and such initiatives should be taken in to consideration for future emerging conditions.

Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study.

BACKGROUND: The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. OBJECTIVE: The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world's largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non-mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. METHODS: We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. RESULTS: We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories "economic stress," "isolation," and "home," while others such as "motion" significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ=-0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. CONCLUSIONS: By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.