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Abstract Background Online health communities (OHCs) provide platforms for patients to seek advice from physicians and receive professional suggestions online. It can improve the efficiency of patients’ diagnosis of simple diseases and alleviate hospital congestion. However, few empirical studies have comprehensively explored the factors influencing patients’ intention to use OHCs through objective data. This study aims to fill this gap by identifying key factors that influence patients’ acceptance of OHCs and proposing effective ways to promote the applications of OHCs in China. Methods Based on the Unified Theory of Acceptance and Usage of Technology (UTAUT), extended with additional constructs identified with patients’ information demands in OHCs, this study developed a research model and proposed nine hypotheses. An online survey involving 783 valid responses was conducted in China to collect data to validate the proposed model. Confirmatory factor analysis and partial least squares (PLS) path model were conducted for instrument validation and hypothesis testing. Results Price value, eHealth literacy, and performance expectancy are the most prominent constructs in the study context. Interestingly, relation quality was also found to have a significant positive relationship with behavioral intention. Conclusions Based on these findings, OHC operators need to create a user-friendly platform, improve information quality, set reasonable prices, and establish consummate security systems. Physicians and related organizations can raise awareness and assist patients in developing the skills to appropriately comprehend and utilize information in OHCs. This study contributes to both technology adoption theory and practice.

As the economy and society develop and the standard of living improves, people's health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT's semantic comprehension with LDA's topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users' emotional reactions and presents valuable insights for delivering personalized health information in online communities. [ABSTRACT FROM AUTHOR]

The Library of Natural Sciences of the Russian Academy of Sciences (RAS) (a division of Pushchino Research Center RAS) has implemented several large-scaled projects in the light of preparing to celebrate the anniversary of the Institute of Protein Research RAS. Among them is a bibliometric analysis of publication activity of all researchers of the Institute including the information on the publications number, international cooperation, highly cited publications, distribution of papers by quartiles of the publishers with providing this information at the library website. Due to the interest shown in invention activities, we have analyzed the patents' thematic areas of the Institute. Another aspect of the study was to set up a portal on scientific schools of the Institute of Protein Research, which highlights the scientific area and schools significance, the leaders' part in every school, scientific papers of schools and its fellows, awards and honours, defended theses, bibliometric analysis of publications. The next research direction was to create a data base on the former researchers who live abroad in order to invite research scientists-compatriots to establish scientific-professional contacts.

Analysts ask managers for forward-looking information in one-third of quarterly conference calls; most frequently, seeking earnings per share guidance. In this study, we examine the long-term consequences of analysts' questions on future manager disclosure choices. Using six types of commonly provided guidance, we find that when analysts request new guidance or ask about prior guidance, managers are more likely to provide similar guidance in future quarters. When analysts do not ask about prior guidance, managers are less likely to provide that type of guidance in the future. This effect is long-lasting, spills over into earnings announcements, and is strongest for firms with the most severe information problems. Our results provide evidence that analysts shape managers' disclosure choices in meaningful ways. [ABSTRACT FROM AUTHOR]