Knowledge, Attitude, and Practice (KAP) toward COVID-19 Pandemic among the Public in Taiwan: A Cross-Sectional Study.

Purpose: Knowledge, attitude, and practice (KAP) models are often used by researchers in the field of public health to explore people's healthy behaviors. Therefore, this study mainly explored the relationships among participants' sociodemographic status, COVID-19 knowledge, affective attitudes, and preventive behaviors. Method: This study adopted an online survey, involving a total of 136 males and 204 females, and used a cross-sectional study to investigate the relationships between variables including gender, age, COVID-19 knowledge, positive affective attitudes (emotional wellbeing, psychological wellbeing, and social wellbeing), negative affective attitudes (negative self-perception and negative perceptions of life), and preventive behaviors (hygiene habits, reducing public activities, and helping others to prevent the epidemic). Results: The majority of participants in the study were knowledgeable about COVID-19. The mean COVID-19 knowledge score was 12.86 (SD = 1.34, range: 7−15 with a full score of 15), indicating a high level of knowledge. However, the key to decide whether participants adopt COVID-19 preventive behaviors was mainly their affective attitudes, especially positive affective attitudes (ß = 0.18−0.25, p< 0.01), rather than COVID-19 disease knowledge (ß = −0.01−0.08, p > 0.05). In addition, the sociodemographic status of the participants revealed obvious differences in the preventive behaviors; females had better preventive behaviors than males such as cooperating with the epidemic prevention hygiene habits (t = −5.08, p< 0.01), reducing public activities (t = −3.00, p< 0.01), and helping others to prevent the epidemic (t = −1.97, p< 0.05), while the older participants were more inclined to adopt preventive behaviors including epidemic prevention hygiene habits (ß = 0.18, p = 0.001, R2 = 0.03), reducing public activities (ß = 0.35, p< 0.001, R2 = 0.13), and helping others to prevent the epidemic (ß = 0.27, p< 0.001, R2 = 0.07). Conclusions: Having adequate COVID-19 knowledge was not linked to higher involvement in precautionary behaviors. Attitudes toward COVID-19 may play a more critical function in prompting individuals to undertake preventive behaviors, and different positive affective attitudes had different predictive relationships with preventive behaviors.

The Relationships among Anxiety, Subjective Well-Being, Media Consumption, and Safety-Seeking Behaviors during the COVID-19 Epidemic.

The COVID-19 epidemic has been confirmed as the largest scale outbreak of atypical pneumonia since the outbreak of severe acute respiratory syndrome (SARS) in 2003 and it has become a public health emergency of international concern. It exacerbated public confusion and anxiety, and the impact of COVID-19 on people needs to be better understood. Indeed, prior studies that conducted meta-analysis of longitudinal cohort research compared mental health before versus during the COVID-19 pandemic and proved that public health polices (e.g., city lockdowns, quarantines, avoiding gatherings, etc.) and COVID-19-related information that circulates on new media platforms directly affected citizen's mental health and well-being. Hence, this research aims to explore Taiwanese people's health status, anxiety, media sources for obtaining COVID-19 information, subjective well-being, and safety-seeking behavior during the COVID-19 epidemic and how they are associated. Online surveys were conducted through new media platforms, and 342 responses were included in the analysis. The research results indicate that the participants experienced different aspects of COVID-19 anxiety, including COVID-19 worry and perceived COVID-19 risk. Among the given media sources, the more participants searched for COVID-19 information on new media, the greater they worried about COVID-19. Furthermore, COVID-19 worry was positively related to safety-seeking behavior, while perceived COVID-19 risk was negatively related to subjective well-being. This paper concludes by offering some suggestions for future studies and pointing out limitations of the present study.

A novel corpus-based computing method for handling critical word-ranking issues: An example of COVID-19 research articles.

A corpus is a massive body of structured textual data that are stored and operated electronically. It usually combines with statistics, machine learning algorithms, or artificial intelligence (AI) technologies to explore the semantic relationship between lexical units, and beneficial when applied to language learning, information processing, translation, and so forth. In the face of a novel disease, like, COVID-19, establishing medical-specific corpus will enhance frontline medical personnel's information acquisition efficiency, guiding them on the right approaches to respond to and prevent the novel disease. To effectively retrieve critical messages from the corpus, appropriately handling word-ranking issues is quite crucial. However, traditional frequency-based approaches may cause bias in handling word-ranking issues because they neither optimize the corpus nor integrally take words' frequency dispersion and concentration criteria into consideration. Thus, this paper develops a novel corpus-based approach that combines a corpus software and Hirsch index (H-index) algorithm to handle the aforementioned issues simultaneously, making word-ranking processes more accurate. This paper compiled 100 COVID-19-related research articles as an empirical example of the target corpus. To verify the proposed approach, this study compared the results of two traditional frequency-based approaches and the proposed approach. The results indicate that the proposed approach can refine corpus and simultaneously compute words' frequency dispersion and concentration criteria in handling word-ranking issues.