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Background: Emergency departments (ED) nurses experience high mental workloads because of unpredictable work environments; however, research evaluating ED nursing workload using a tool incorporating nurses' perception is lacking. Quantify ED nursing subjective workload and explore the impact of work experience on perceived workload.
Methods: Thirty-two ED nurses at a tertiary academic hospital in the Republic of Korea were surveyed to assess their subjective workload for ED procedures using the National Aeronautics and Space Administration Task Load Index (NASA-TLX). Nonparametric statistical analysis was performed to describe the data, and linear regression analysis was conducted to estimate the impact of work experience on perceived workload.
Results: Cardiopulmonary resuscitation (CPR) had the highest median workload, followed by interruption from a patient and their family members. Although inexperienced nurses perceived the 'special care' procedures (CPR and defibrillation) as more challenging compared with other categories, analysis revealed that nurses with more than 107 months of experience reported a significantly higher workload than those with less than 36 months of experience.
Conclusion: Addressing interruptions and customizing training can alleviate ED nursing workload. Quantified perceived workload is useful for identifying acceptable thresholds to maintain optimal workload, which ultimately contributes to predicting nursing staffing needs and ED crowding.
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http://dx.doi.org/10.1016/j.ienj.2024.101424 | DOI Listing |
BMC Infect Dis
September 2025
Department Health and Prevention, Institute of Psychology, University of Greifswald, Robert-Blum-Str. 13, Greifswald, 17489, Germany.
Background: Healthcare workers (HCWs) played a crucial role in dealing with the COVID-19 pandemic. In addition to increased workloads, they were confronted with stigmatization due to their work in the health sector.
Methods: Guided by the Health Stigma and Discrimination Framework (HSDF), this study aimed to explore the experiences of stigmatization of HCWs in Germany using semi-structured interviews (N = 34) and investigate effective coping strategies and existing needs in this context.
Eur J Appl Physiol
September 2025
Department of Occupational Health, Psychology, and Sports Sciences, University of Gavle, Gävle, Sweden.
Aim: To summarize the literature on quantitative measures of physical demands in eldercare, with attention to differences between temporary and permanent workers, and to identify gaps to guide future physiological research.
Methods: We searched Scopus, Web of Science, and PubMed for English and Swedish peer-reviewed studies on physical demands in eldercare. Risk of bias was assessed, and descriptive data extracted.
JMIR Res Protoc
September 2025
School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Background: Electronic health records (EHRs) have been linked to information overload, which can lead to cognitive fatigue, a precursor to burnout. This can cause health care providers to miss critical information and make clinical errors, leading to delays in care delivery. This challenge is particularly pronounced in medical intensive care units (ICUs), where patients are critically ill and their EHRs contain extensive and complex data.
View Article and Find Full Text PDFInt J Nurs Stud
September 2025
KEMRI-Wellcome Trust Research Program, Nairobi, Kenya.
Background: Nurses remain critical in newborn care delivery in Kenya. However, persistent nurse shortages in newborn units limit their ability to provide optimal care. Staff shortages contribute to missed care and high workloads, negatively impacting the motivation and well-being of nurses.
View Article and Find Full Text PDFNurse Educ Pract
September 2025
School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Aim: This study aimed to systematically review and synthesize the most recent qualitative studies on frontline nurses' insights and perspectives regarding the use of artificial intelligence (AI) tools in their clinical practice in hospital settings.
Background: There is limited information on frontline nurses' perceptions, attitudes and expectations regarding the adoption of AI in healthcare.
Design: A systematic review and thematic synthesis of qualitative evidence was conducted.