Background: COVID-19 made it mandatory for Namibian education institutions to transition from traditional face-to-face classroom learning to online learning. Minimal time was available to prepare nursing students to adopt this model of learning, which subsequently influenced their learning experiences.
Aim: The aim of the study was to explore and describe nursing students' experiences regarding their preparedness to migrate to online learning during the COVID-19 lockdown at a public university in Namibia.
Setting: Semi-structured interviews were conducted in English at the public university in Kavango East, Namibia.
Methods: A qualitative approach utilising an exploratory and descriptive design was used. Convenience sampling and a semi-structured interview guide was used to assess the experiences of undergraduate nursing students. Data saturation was achieved after 15 interviews. ATLAS.ti 8 software assisted with management of data that was analysed inductively following the six steps of thematic analysis.
Results: The following themes emerged from analysis of the data: (1) students' readiness to migrate to online learning; (2) challenges faced by nursing students during the migration to online learning; and (3) strategies to support the transition from face-to-face to online learning.
Conclusion: The study's findings show that the student nurses were unprepared for online learning due to lack of skills and the ability to use technology to navigate online learning platforms. Access to online learning was also hampered by poor Internet connectivity and unreliable electronic devices.
Contribution: These findings may be used to develop targeted interventions and strategies to mitigate challenges faced during transition from face-to-face to online learning.
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http://dx.doi.org/10.4102/hsag.v28i0.2427 | DOI Listing |
BMC Nurs
January 2025
College of Nursing, Keimyung University, Daegu, South Korea.
Background: The incidence of acute cardiac arrest is increasing and is directly linked to patient survival, highlighting the critical role of nurses. Advanced nursing skills for cardiac arrest patients, such as self-directed pre-learning applied to various clinical situations, require a systematic blended learning approach to integrate knowledge and enhance clinical performance through face-to-face practice. The purpose of this study is to evaluate the effectiveness of a blended simulation education program for Advanced Cardiac Life Support (ACLS) using the PARTNER model.
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January 2025
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
This study investigates pilot perspectives on the use of Flight Simulation Training Devices (FSTDs) in Canada's general aviation (GA) sector, which, despite their longstanding adoption, remain underutilized. It also examines pilot perspectives on the potential of Augmented Reality (AR) technology as an assistive tool in GA pilot training. An online survey gathered views on FSTD use for routine flight operations and emergency training, as well as AR's potential to support learning.
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January 2025
Key Laboratory of Water and Sediment Sciences, Ministry of Education, College of Environmental Sciences and Engineering, Peking University, Beijing 100871 China; State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, Beijing 100871 China. Electronic address:
Water diversion projects effectively mitigate the uneven distribution of water resources but can also influence aquatic biodiversity and ecosystem functions. Despite their importance, the impacts of such projects on multi-domain microbial community dynamics and the underlying mechanisms remain poorly understood. Utilizing high-throughput sequencing, we investigated bacterial, archaeal, and fungal community dynamics along the eastern route of the South-to-North water diversion project during both non-water diversion period (NWDP) and water diversion period (WDP).
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January 2025
IMT Atlantique, Lab-STICC, UMR CNRS 6285, team RAMBO, F-29238 Brest, France.
Rehabilitation is the process of helping people regain or improve lost or impaired function due to injury, illness, or disease. To assist in tracking the progress of patients undergoing rehabilitation, this paper proposes a lightweight graph-based deep-learning model for the automatic assessment of physical rehabilitation exercises. The model takes as input the 3D skeleton sequence of a patient performing a movement and outputs a continuous quality score, as a means for patient supervision that could complement or even substitute the need for ordinary clinical exams.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, London, Surrey, SM2 5PT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
This study aims to develop and evaluate a fast and robust deep learningbased auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets. Approach: This study was conducted in two phases.
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