Emotional intelligence (EI) is increasingly recognized as a key factor in healthcare, where managing emotions is vital for job satisfaction, productivity, and interpersonal relationships. For nurses, particularly during the COVID-19 pandemic, EI plays a pivotal role in navigating emotional challenges and improving their quality of work life (QoWL). This study examined how EI moderates the relationship between nurses' preparedness to care for COVID-19 patients and their QoWL. A cross-sectional, correlational design was used, involving 267 nurses from various healthcare settings. Data were collected through the Emotional Intelligence Scale, the Quality of Nursing Work Life survey, and demographic questionnaires. The sample was predominantly female (94.4%), with a mean age of 37.47 years (SD = 8.09) and an average of 8.43 years of experience (SD = 6.33). Most nurses (87.3%) attended COVID-19-related workshops, with 76.4% feeling prepared to care for COVID-19 patients. Emotional intelligence levels were high, with 93.6% of nurses reporting good personal competence and 85.4% reporting good social competence. Descriptive results showed that 71% of nurses perceived their QoWL as good, while 29% rated it as fair. Pearson correlation analysis revealed significant positive correlations between both personal competence (r = 0.33, < 0.001) and social competence (r = 0.34, < 0.001) with QoWL, but preparedness to care for COVID-19 patients did not correlate significantly with either EI or QoWL. Hierarchical regression analysis demonstrated that, although nurses' preparedness alone did not predict QoWL (β = 0.034, = 0.57), including emotional intelligence as a moderator explains 41% of the variance in QoWL. Both personal (β = 0.578, < 0.001) and social competence (β = 0.665, < 0.001) components of EI had significant buffering effects on the relationship between preparedness and QoWL. These findings suggest that fostering EI in nurses can enhance their resilience and improve their work life quality, particularly in high-stress healthcare environments like those experienced during the pandemic.
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http://dx.doi.org/10.3390/bs14121166 | DOI Listing |
Nurs Open
January 2025
Huaihe Hospital of Henan University, Kaifeng City, Henan Province, China.
Aim: To explore the influence of emotional intelligence and organisational commitment (OC) on clinical nurses' turnover intention (TI) and to provide intervention strategies to reduce the turnover rate of nursing staff and maintain the stability of the nursing team.
Design: A cross-sectional descriptive study was conducted with nurses (n = 452) in a tertiary hospital in Kaifeng City, Henan Province, China.
Methods: The project was conducted in July 2023.
Front Public Health
January 2025
College of Business Administration, Chongqing Vocational and Technical University of Mechatronics, Chongqing, China.
Purpose: This study aims to examine how government fiscal and tax incentives facilitate the development and application of green technologies, promoting corporate environmental responsibility and improving public health and hygiene.
Methods: The study utilizes empirical data from listed enterprises in the new energy automobile industry between 2018 and 2023. A multiple regression model is used to assess the effects of government subsidies and tax incentives on green technological innovation and enterprise growth, controlling for various factors such as enterprise size and R&D investment.
Front Public Health
January 2025
Outpatient Department, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an, China.
Background: Ethical leadership is crucial in nursing management, and self-compassion is increasingly recognized as a significant factor influencing nurses' job performance. Although the link between ethical leadership and nurse job performance has been established, the specific mechanisms that underlie this relationship remain unclear. Additionally, there is a paucity of research examining the potential role of self-compassion in this context.
View Article and Find Full Text PDFPeerJ Comput Sci
December 2024
Information and Computer Science Department, King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Given the integration of color emotion space information from multiple feature sources in multimodal recognition systems, effectively fusing this information presents a significant challenge. This article proposes a three-dimensional (3D) color-emotion space visual feature extraction model for multimodal data integration based on an improved Gaussian mixture model to address these issues. Unlike traditional methods, which often struggle with redundant information and high model complexity, our approach optimizes feature fusion by employing entropy and visual feature sequences.
View Article and Find Full Text PDFInt J Ment Health Nurs
February 2025
Inholland University of Applied Sciences, Amsterdam, Netherlands.
In mental healthcare, therapists' empathy and mentalizing are associated with better opportunities to establish positive working relations with patients. The present study aimed to explore mental health nurses' level of empathy and mentalizing (compared with reference groups studying or working in different contexts), the association between mental health nurses' level of empathy and mentalizing and sociodemographic characteristics of these nurses, and the association between mental health nurses' level of empathy and mentalizing. A cross-sectional design was used in adherence with the Strengthening the Reporting of Observational Studies in Epidemiology statement.
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