The current study aimed to address the limitations of the terror management theory literature by using big data analysis to examine the theory's predictions in the COVID-19 pandemic. Specifically, Google Trends were examined before and after the first COVID-19 case was identified in Singapore. The results showed that there was a significant increase in mortality salience, intergroup conflict, and prosocial behavior, and a significant decrease in materialism after the first COVID-19 case was identified. However, no significant differences were found for anxiety. Limitations include the assumption that search terms reflect intentions that would eventually lead to a relevant behavior and the lack of data from other sources to corroborate with the results from Google Trends. Future research could use data from other sources to examine the effects of COVID-19 on theoretically relevant behaviors.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024090 | PMC |
| http://dx.doi.org/10.1177/00302228221092583 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Rhythm Control Better Prevents Stroke than Rate Control in Patients with Concomitant Hypertrophic Cardiomyopathy and Atrial Fibrillation: A Nationwide Population Based Cohort Study with Long-Term Follow-Up.
Acta Cardiol Sin
January 2025
Cardiovascular Center, Taichung Veterans General Hospital, Taichung.
Background: Atrial fibrillation (AF) increases the risks of stroke and mortality. It remains unclear whether rhythm control reduces the risk of stroke in patients with AF concomitant with hypertrophic cardiomyopathy (HCM).
Methods: We identified AF patients with HCM who were ≥ 18 years old in the Taiwan National Health Insurance Database.
Best Practices for Big Data Sources and Methods in Surgery.
JAMA Surg
January 2025
Departments of Surgery and Biomedical Engineering, University of Virginia School of Medicine, Charlottesville.
Practical Guide to Image-Based Big Data Research.
JAMA Surg
January 2025
Department of Surgery, Stanford University School of Medicine, Palo Alto, California.
Practical Guide to Clinical Big Data Sources.
JAMA Surg
January 2025
Department of Surgery, Veterans Affairs Boston Health Care System, Boston, Massachusetts.
Leveraging feature extraction and risk-based clustering for advanced fault diagnosis in equipment.
PLoS One
December 2024
Department of Industrial Engineering, Inha University, Incheon, South Korea.
In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk remains underexplored. The current practice of failure risk diagnosis is impeded by the manual intervention of managers, leading to varying evaluations for identical products or similar facilities.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!