Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach

Exploring sources of patient dissatisfaction in mobile health communication: A text analysis based on structural topic model

Understanding online patient dissatisfaction is essential for improving the quality of healthcare services, patient satisfaction, and physician career development. This study is the first to apply the structural topic model to patient satisfaction research based on patient online reviews from a mobile health communication platform, revealing eight negative topics of patient concerns. These topics include under-explored areas such as "go to the hospital for check-ups," "incomplete counseling," and "language expression." Additionally, we incorporated the doctor's title as a covariate in the model to examine how specific topics varied across different conditions. The results indicated that higher-titled doctors were more likely to receive complaints about the cost of treatment and whether the question was answered, whereas lower-titled doctors were more likely to receive complaints related to physician's knowledge, incomplete counseling, and response speed. This study not only enhances our understanding of mobile health services but also provides targeted insights for healthcare providers to improve their services, thereby contributing to the advancement of patient-centered care.

Mental health status among non-medical college students returning to school during the COVID-19 pandemic in Zhanjiang city: A cross-sectional study

The coronavirus disease-2019 (COVID-19) pandemic has brought huge and continuous damage to mental health. The mental health of non-medical college students after returning to school remains largely unknown and the influencing factors were awaited to be deciphered. This cross-sectional study was launched among 1,083 non-medical students in Zhanjiang city by means of online survey (WeChat App) from August 1st, 2022 to August 7th, 2022. Knowledge about COVID-19 and attitude toward COVID-19 were assessed by using 7-items and 5-items questionnaires, respectively. Sleep quality, anxiety and depression symptoms were evaluated by Pittsburgh sleep quality index (PSQI), Hamilton depression rating scale-17 (HDRS-17) and self-rating anxiety scale (SAS), respectively. The results showed that more than half of the participants were knowledgeable about COVID-19. The majority of the participants held positive attitude toward COVID-19. The data demonstrated that 6.8% students had poor sleep quality, and 1.86, 0.37 and 0.37% students had mild, moderate and severe anxiety, respectively. About 26.7, 4.7 and 1.7% students had minimal, mild-moderate and severe depression. Female students showed higher proportions of anxiety (p = 0.02) and depression (p < 0.0001) than male students. Students with monthly household income below 3,000 RMB were more vulnerable to anxiety (p = 0.017) and depression (p = 0.004). Correlation analysis and Multivariate logistic regression analysis results showed that lower grade was positively related with anxiety and depression. Female students, income lower than 3,000 RMB/month, poor knowledge about COVID-19 and negative attitude toward COVID-19 were associated with insomnia, anxiety and depression. This study indicated that during the COVID-19 pandemic, a majority of non-medical students returning to school remained good sleep quality and a small number of students suffered from depression and anxiety. To our knowledge, this is a novel study revealing the mental health of non-medical college students concerning COVID-19 in Zhanjiang.

Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment (n = 31,661, 34.44%), fear of COVID-19 (n = 22,597, 24.58%), individual characteristic (n = 17,688, 19.24%), government support (n = 15,482, 16.84%), and community effect (n = 4,505, 4.90%). The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic.