Method: This was a quasi-experimental study carried out among nurses stationed in municipal care units in the interior of São Paulo State. Data were collected during the course of a pedagogical workshop that used low-fidelity simulation training.
Results: The study included 42 nurses, mostly female with over 15 years of experience. After low-fidelity simulation training, nurses showed a significant increase (p<0.05) in confidence related to nursing care in urinary retention. The lowest self-attributed scores during the activity were related to the objective assessment of urinary retention.
Conclusion: Low-fidelity simulated training is an effective resource for the development of nurses with respect to nursing care in urinary retention.
Objetivo: Avaliar o nível de autoconfiança de enfermeiros na assistência de enfermagem na retenção urinária antes e após atividade simulada de baixa fidelidade.
MÉtodo: Estudo quase-experimental realizado junto aos enfermeiros lotados nas unidades de atendimento de município do interior do estado de São Paulo. Os dados foram coletados durante a realização de uma oficina pedagógica que contou com atividade simulada de baixa fidelidade.
Resultados: Participaram do estudo 42 enfermeiros, a maioria do sexo feminino e com mais de 15 anos de atuação. Após o treino simulado de baixa fidelidade os enfermeiros demonstraram aumento significativo (p < 0,05) na autoconfiança relacionada à assistência de enfermagem na retenção urinária. Os menores escores autoatribuídos durante a atividade foram relacionados à avaliação objetiva da retenção urinária.
ConclusÃo: A simulação de baixa fidelidade é um recurso efetivo no aprimoramento de enfermeiros no que diz respeito à assistência de enfermagem na retenção urinária.
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http://dx.doi.org/10.1590/S0080-623420160000600017 | DOI Listing |
Indian Pediatr
January 2025
Department of Neonatology, St John's Medical College Hospital, Bangalore, Karnataka, India.
Introduction: Neonatal intensive care unit (NICU) graduates are at risk of sudden death at home after discharge. Many of these deaths can be prevented if parents can identify warning signs and provide immediate resuscitation.
Objectives: The primary objective of this study was to assess the feasibility of training parents of high-risk neonates in low- and middle-income countries (LMICs) to deliver infant resuscitation effectively.
Cureus
November 2024
Medical Education and Simulation, Maidstone and Tunbridge Wells National Health Service (NHS) Trust, Kent, GBR.
Background Many newly qualified doctors feel unprepared for clinical practice. The literature identifies themes including difficulties with clinical reasoning, emergency management, handover, and prioritization of tasks. Although there is an expected level of anxiety for newly qualified doctors, this appears to be amplified with respect to the first on-call shifts that encompass these themes.
View Article and Find Full Text PDFSci Rep
December 2024
National Institute for Fusion Science, Toki, Gifu, 509-5292, Japan.
Plast Reconstr Surg Glob Open
December 2024
From the Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, PA.
Background: The complex skills required to perform microsurgery are primarily taught in the high-stakes environment of the operating room. However, learners would benefit from developing these abilities in lower-stakes environments beforehand, allowing them to focus on higher-level tasks intraoperatively. This article outlines available resources for developing microsurgical skills outside the operating room and evaluates their alignment with best practices for performance enhancement, thereby identifying ways to improve microsurgical education.
View Article and Find Full Text PDFJ Environ Manage
December 2024
Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, 3010, Australia.
The destructive and life-threatening nature of flood events calls for fast and accurate methods to predict dynamic flood behaviour. Data-driven surrogate models have been developed to quickly predict flood inundation, though their accuracy relies on the available flood information for model training and validation. Flood observations are rarely available at high spatial and temporal scales, and thus computationally expensive high-resolution hydrodynamic (high-fidelity) models are often used to generate training data through simulation of selected flood events.
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