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http://dx.doi.org/10.1097/CCM.0b013e31828ce91b | DOI Listing |
Int Nurs Rev
March 2025
College of Nursing, ShaoYang University, Shaoyang, China.
Background: While numerous studies have quantified the prevalence of reasons for missed care, a comprehensive synthesis of evidence across various health systems remains lacking.
Aim: To estimate the pooled prevalence of the reasons reported by nurses for missed care, using data from the MISSCARE surveys.
Introduction: Missed nursing care, which refers to any aspect of essential patient care that is omitted or delayed, presents substantial risks to patient safety and the quality of care.
HNO
January 2025
Deutsches Hörzentrum der Medizinischen Hochschule Hannover, Karl-Wiechert-Allee 3, 30625, Hannover, Deutschland.
Treatment of patients with severe hearing loss or deafness using cochlear implants (CI) is nowadays clinical routine. In the Hannover Medical School alone, more than 500 patients are treated with CI annually, meaning that the pool of patients with CI increases significantly each year. Worldwide, there are over 1 million patients with a CI system; in Germany the figure is estimated at over 60,000.
View Article and Find Full Text PDFAust Occup Ther J
February 2025
Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia.
Introduction: Driving safety may be compromised in people with dementia or mild cognitive impairment (MCI). Occupational therapists assess and screen for driving safety in older people with cognitive impairment. However, little is known about their perspectives relating to these assessments.
View Article and Find Full Text PDFJ Adv Nurs
January 2025
Clinical Nursing Research Unit, Aalborg University Hospital, Aalborg, Denmark.
Aim: To explore nurses' perceptions of reasons for missed nursing care.
Design: A multicentre qualitative descriptive study was undertaken from August 2022 to January 2023.
Methods: Interpretive description methodology was used.
Comput Biol Med
January 2025
INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FCTUC - Faculty of Sciences and Technology of the University of Coimbra, Coimbra, Portugal. Electronic address:
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%-40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation.
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