Burnout, negative emotions, and wellbeing among social workers in China after community lockdowns during the COVID-19 pandemic: Mediating roles of trait mindfulness

Objective This study aimed to investigate burnout situation of social workers (SWs) who experienced the COVID-19 pandemic-related community lockdown 1 year before, and to assess the protective value of trait mindfulness (TM) in states of burnout. Method We surveyed the burnout, trait mindfulness, negative emotions (NEs) and wellbeing (WB) of 182 social workers provided services to Wuhan lockdowns community by COVID-19 one year before. Burnout were measured using the Maslach Burnout Inventory–Human Services Survey;TM using the Mindful Attention Awareness Scale;NEs using the Depression Anxiety and Stress Scale-21;and WB using the General Wellbeing Schedule. We also performed correlation regression analysis and mediation test for burnout, TM, NEs, and WB. Results Among the 182 respondents, 75 (41.2%) still suffered from severe burnout. TM was negatively correlated with burnout (r = −0.623), negatively correlated with NEs (r = −0.560), and positively correlated with WB (r = 0.617). Burnout had a significantly positive correlation with NEs (r = 0.544) and a significantly negative correlation with WB (r = −0.666). Further, WB had significantly negative correlation with NEs (r = −0.758). After controlling for age, gender, marital status, educational level, and years of employment, burnout had a significantly positive predictive effect on NEs (β = 0.509), whereas TM had a significantly negative predictive effect on NEs (β = −0.334). TM played a partial mediating role in the effect of burnout on NEs, with a mediating effect and effect ratio of 0.088 and 39.7%, respectively. Burnout had a significantly negative predictive effect on WB (β = −0.598), whereas TM had a significantly positive predictive effect on WB (β = 0.299). TM played a partial mediating role in the effect of burnout on NEs, with a mediating effect and effect ratio of −0.164 and 30.3%, respectively. WB had a significantly negative predictive effect on NEs (β = −0.711), and it played a partial mediating role in the effect of burnout on NEs, with a mediating effect and effect ratio of 0.185 and 83.3%, respectively. Conclusion The current levels of burnout among local SWs remained high 1 year after the community lockdowns. TM played a mediating role in the relationship between burnout, NEs, and WB. Concomitantly, WB played a mediating role in the relationship between burnout and NEs. Therefore, in the context of burnout, TM is a protective factor for reducing emotional stress and risks of developing psychiatric disorders through the enhancement of WB.

A multilingual dataset of COVID-19 vaccination attitudes on Twitter.

Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. A fast and accurate grasp of public attitudes toward vaccination is critical to addressing vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualization of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes in public COVID-19 vaccination attitudes.

Exploring Spillover Effects for COVID-19 Cascade Prediction.

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

Associations of Mental Health and Personal Preventive Measure Compliance With Exposure to COVID-19 Information During Work Resumption Following the COVID-19 Outbreak in China: Cross-Sectional Survey Study.

BACKGROUND: Risk and crisis communication plays an essential role in public health emergency responses. The COVID-19 pandemic has triggered spontaneous and intensive media attention, which has affected people's adoption of personal preventive measures and their mental health. OBJECTIVE: The aim of this study was to investigate the associations between exposure to COVID-19-specific information and mental health (depression and sleep quality) and self-reported compliance with personal preventive measures (face mask wearing and hand sanitizing). We also tested whether these associations were moderated by thoughtful consideration of the veracity of the information to which people were exposed. METHODS: A cross-sectional, closed web-based survey was conducted among a sample of 3035 factory workers at the beginning of work resumption following the COVID-19 outbreak in Shenzhen, China. A stratified two-stage cluster sampling design was used for recruitment. Multivariate linear and logistic regression models were used for the analyses. RESULTS: The prevalence of probable moderate-to-severe depression was 170/3035 (5.6%), while that of good or excellent sleep quality was 2110/3035 (69.5%). The prevalence of self-reported consistent face mask wearing in public places was 2903/3035 (95.7%), while that of sanitizing hands every time after returning from public spaces or touching public installations was 2151/3035 (70.9%). Of the 3035 respondents, 1013 to 1638 (33.3% to 54.0%) reported >1 hour of daily exposure to COVID-19-specific information through web-based media and television. After controlling for significant background variables, higher information exposure via television and via newspapers and magazines was associated with better sleep quality and higher compliance with hand sanitizing. Higher exposure via unofficial web-based media was associated with higher compliance with hand sanitizing but was also associated with higher depressive symptoms. In contrast, higher exposure through face-to-face communication was associated with higher depressive symptoms, worse sleep quality, and lower compliance with hand sanitizing. Exposure to information about positive outcomes for patients with COVID-19, development of vaccines and effective treatments, and heroic stories about frontline health care workers were associated with both better mental health and higher compliance with preventive measures. Higher overall information exposure was associated with higher depressive symptoms among participants who were less likely to carefully consider the veracity of the information to which they were exposed; it was also associated with better sleep quality among people who reported more thoughtful consideration of information veracity. CONCLUSIONS: This study provides empirical evidence of how the amount, sources, and contents of information to which people were exposed influenced their mental health and compliance with personal preventive measures at the initial phase of work resumption in China. Thoughtful consideration of information quality was found to play an important moderating role. Our findings may inform strategic risk communication by government and public health authorities during the COVID-19 pandemic.