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Background: Inappropriately selected models of nursing care delivery and emotional exhaustion of nurses at work not only affect the nurses' health, but also the health of their patients.
Purpose: To examine the relationship between nursing care delivery models, nurses' emotional exhaustion, and quality of nursing care.
Methods: A cross-sectional survey that used a convenience sampling technique was employed. A total of 160 participants completed the study. Participants provided information about nursing care delivery models, nurses, emotional exhaustion, and quality of nursing care.
Results: Participants had a moderate level of emotional exhaustion. No statistically significant difference in the scores of quality of nursing care and emotional exhaustion were found according to the type of care delivery model ( > .05). Emotional exhaustion was significantly and negatively correlated with the (nursing staff characteristics) subscale of quality of nursing care ( = -.183, = .021). There was a significant difference in emotional exhaustion in regard to shift duty, marital status, education level, years of experience, salary, and working area). Only marital status significantly predicted emotional exhaustion ( = 2.57, = -8.98, = .011).
Conclusions: Quality of nursing care was associated with nurses' emotional exhaustion rather than models of nursing care delivery. As nurses' emotional exhaustion could negatively affect the quality of nursing care, addressing the emotional exhaustion of nurses is important to improve patient outcomes.
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http://dx.doi.org/10.1177/23779608221124292 | DOI Listing |
Asian Nurs Res (Korean Soc Nurs Sci)
September 2025
Affiliated Hospital of Jinggangshan University, Ji'an, China. Electronic address:
Background: While digital transformation has become a necessary trend for hospitals, it imposes technostress on nurses working on the health-care front lines. According to previous research, it increases conflict between nurses and patients but the mechanism and age difference are ignored.
Methods: A total of 672 nurses recruited from five hospitals in Liaoning Province, China.
J Med Internet Res
September 2025
Department of Medicine, David Geffen School of Medicine, University of California, 11301 Wilshire Blvd, Los Angeles, CA, 90073, United States, 1 3104783711 ext. 44860.
Background: Telehealth use, including video visits and secure messages, expanded significantly in Veterans Health Administration (VHA) primary care during the COVID-19 pandemic. However, primary care provider (PCP) burnout also increased during this period. Each modality may have affected primary care workloads differently (either by substituting for or complementing in-person visits) and thereby had varying effects on PCP burnout.
View Article and Find Full Text PDFFront Public Health
September 2025
China Institute of Rural Education Development, Northeast Normal University, Changchun, China.
Introduction: Teacher burnout poses a significant threat to the sustainability of rural education. However, the effect of out-of-field teaching as a job demand remains understudied. This study applies the Job Demands-Resources (JD-R) model to explore how job demands, job resources, and personal resources interact with burnout among rural teachers.
View Article and Find Full Text PDFZdr Varst
September 2025
Healthcare System Centre, National Institute of Public Health, Trubarjeva cesta 2, 1000 Ljubljana, Slovenia.
Aim: To investigate the reasons for leaving the hospital and recommending the hospital among nurses employed at internal diseases and surgical departments.
Methods: A cross-sectional explorative design was employed. Eight general hospitals and two clinical centres participated in the study.
Acta Psychol (Amst)
September 2025
College of Fine Arts, Huaqiao University, Quanzhou, China. Electronic address:
The application of artificial intelligence technology has significantly enhanced the operational efficiency of companies, but it has also brought pressure related to job replacement and technological upgrading, leading to anxiety among employees regarding artificial intelligence. This kind of anxiety has a profound impact on employees' work passion, yet currently, there are relatively few researches on this area, making further exploration necessary. This study obtained necessary data by distributing questionnaires to 430 employees in manufacturing companies and conducted empirical analysis to examine how employees' anxiety about artificial intelligence affects their work passion.
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