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Background: Nursing workload is largely studied but poorly explored under physical, mental, and emotional dimensions. Currently, only a limited number of variables have been linked to nursing workload and work contexts.
Purpose: The study aimed to investigate whether it is feasible to identify variables that consistently correlate with nursing workload and others that are specific to the context.
Methods: We employed a descriptive correlational analysis and a cross-sectional design. Data were collected through a survey distributed to registered nurses working across Italy, at the conclusion of randomly assigned morning or afternoon shifts.
Results: We received 456 surveys from 195 shifts, collected from nurses in four public and two private hospitals. Commonly associated variables with nursing workload dimensions included patient complexity of care, admission/discharge or transfer, informing patients/relatives, contacting physicians, and unscheduled activities. Variables categorized as setting-specific were patient isolation and specialties, nurse-to-patient ratio, adequacy of staff in the shift, peer collaboration, healthcare documentation, educating others, and medical urgency.
Conclusions: In summary, certain variables consistently correlate with nursing workload across settings, while others are specific to the context of care. It is imperative for nurses and nurse managers to measure the nursing workload in various dimensions, enabling the prompt implementation of improvement actions.
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http://dx.doi.org/10.1177/23779608241258564 | 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.