A Clinical Prediction Model For Short-Term Prognosis In Patients With Non Acute Myocardial Infarction Related Cardiogenic Shock.

Background: While acute myocardial infarction (AMI) is widely recognized as the primary cause of Cardiogenic Shock (CS), Non-AMI related CS has been excluded from the majority of CS studies. Information on its prognostic factors remains largely understudied, and it is necessary to focus on these patients to identify the specific risk factors. In this study, we aimed to build and validate a predictive nomogram and risk classification system.

Communicating Nutritional Knowledge to the Chinese Public: Examining Predictive Factors of User Engagement on TikTok in China.

Objective: This study aims to identify content variables that theoretical research suggests should be considered as strategic approaches to facilitate science communication with the public and to assess their practical effects on user engagement metrics.

Methods: Data were collected from the official Chinese TikTok account (Douyin) of the Nutrition Research Institute of China National Cereals, Oils and Foodstuffs Corporation, China's largest state-owned food processing conglomerate. Dependent variables included likes, shares, comments, subscription increases.

I know my teammates: the role of Group Member Familiarity in Computer-Supported and face-to-face collaborative learning.

Considering the importance of group member familiarity in collaborative learning in classroom learning environments, this study examined the impact of group member familiarity on CSCL (computer-supported collaborative learning) in a networked setting. Also, the differences between CSCL in the online environments and FtF (face-to-face) collaborative learning were also compared. An analysis using structural equation modeling revealed that group member familiarity increased teamwork satisfaction, which in turn increased student engagement and perceived knowledge construction.

Sentiment Impact of Public Health Agency communication Strategies on TikTok under COVID-19 Normalization: Deep Learning Exploration.

Aim: The accessibility of social media data has allowed researchers to measure official-public interactions during COVID-19. However, previous work analyzing official posts or public comments has failed to explore the link between the two. Therefore, this study investigates the relationship between the communication strategies of public health agencies (PHAs) on TikTok and public emotional/sentiment tendencies in COVID-19 normalization.

Correction to: Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

[This corrects the article DOI: 10.1007/s10389-023-01833-4.].

How does online social interaction promote students' continuous learning intentions?

Learning from online videos using synchronized Danmu comments provides a co-learning experience. To explore the factors influencing learning with or without Danmu videos, an initial set of reasons and impediments was compiled based on a pilot study of 24 Chinese university students with learning experience using Danmu videos. Three hundred such students were surveyed to determine the factors that influence their motivations and hindrances with regard to using Danmu videos.

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown.

Subject And Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages.

How public health agencies communicate with the public on TikTok under the normalization of COVID-19: A case of 2022 Shanghai's outbreak.

Objective: As life with COVID-19 became a norm in 2022, the public's demand for and perception of COVID-19-related information has changed. This study analyzed the performance and responses of Healthy China and the public at various stages of COVID-19 normalization using the crisis and emergency risk communication (CERC) theory.

Methods: This study was based on the 2022 Shanghai COVID-19 outbreak and data from "Healthy China," the official TikTok account of the National Health Commission of the People's Republic of China (NHCC).

A Visualization Analysis of Crisis and Risk Communication Research Using CiteSpace.

This study aims to understand the research status and development trend of crisis and risk communication research (CRCR) through a visual analysis in CiteSpace, thereby providing a more comprehensive perspective for future research agenda. First, we retrieved published papers from Web of Science (1986-2020) and Scopus (1979-2020) with a title search. Subsequently, we analyzed the main research strengths and main topics of CRCR from two dimensions: co-authorship network and co-citation network.

Examining Crisis Communication Using Semantic Network and Sentiment Analysis: A Case Study on NetEase Games.

The mobile game "Immortal Conquest," created by NetEase Games, caused a dramatic user dissatisfaction event after an introduction of a sudden and uninvited "pay-to-win" update. As a result, many players filed grievances against NetEase in a court. The official game website issued three apologies, with mix results, to mitigate the crisis.