Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.

Community resilience and anxiety among Chinese older adults during COVID-19: The moderating role of trust in local government.

The worldwide spread of COVID-19 has resulted in an enormous threat to public health, causing global panic, especially older adults suffering severe anxiety due to their vulnerability. With a questionnaire survey on 213 Chinese older adults in April 2020, we examined the role of community resilience in protecting older adults from anxiety during the COVID-19 pandemic, and simultaneously considered the moderating role of trust in local government. The results indicated that community resilience was negatively associated with older adults' anxiety, and this association was weakened for older adults with low trust in local government. This study has implications for intervention designs that combine resilient factors related to communities and local governments to relieve older adults' anxiety during the pandemic. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Community resilience and psychological distress in Chinese older adults amid COVID-19: the roles of perceived community prevention effectiveness and risk perception.

Objectives: The COVID-19 pandemic is sweeping the world, bringing a disaster for not only public physical but also mental health. As older adults are more vulnerable than those in other age groups in this disaster, their psychological distress in this pandemic is of particular concern. Considering the importance of communities in combating the pandemic, we examined the role of a community in relieving older adults' psychological distress and the heterogeneous effect by older adults' different levels of risk perception.Method: We collected data through a questionnaire survey of 272 older adults in 12 communities in the Sichuan province of China in April 2020, and used the structural equation model to analyze the data.Results: we found that community resilience was negatively associated with older adults' psychological distress (depression, anxiety, stress), and this association was mediated by perceived community prevention effectiveness. However, this indirect effect was stronger for older adults with low risk perceptions than for ones with high risk perceptions (including self-risk perception and group-risk perception).Conclusion: These findings underlined a community's vital significance in alleviating older adults' psychological distress amid the COVID-19 pandemic and moreover indicated that tailored interventions for facilitating community pandemic prevention should be designed for older adults with different risk perceptions.