In digital learning, the use of an interactive eBook is perceived as helpful for students. However, the effect of interactive eBooks on learning among clinical nurses has not been explored yet. This study used an interactive electrocardiogram eBook to explore the effect of digital learning on the promotion of electrocardiogram interpretation competence, confidence, knowledge retention, and learning satisfaction among clinical nurses. A single-group quasi-experimental study with three repeated measures was conducted. A total of 80 nurses from the emergency room, critical units, and medical-surgical units completed the measures. The results showed that digital learning is an effective method that significantly improved nurses' electrocardiogram competence, learning retention, confidence, and learning satisfaction. Most nurses were satisfied with the convenience and content design of this eBook. Few nurses reported drawbacks regarding loading speed and individual learning habits. It is recommended that more preset learning exercise questions should be created for trial and error so that nurses can have repeated practice for self-assessment. Specific feedback mechanisms should be established to promote motivation for digital self-learning.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/CIN.0000000000000823 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Prototype of AI-powered assistance system for digitalisation of manual waste sorting.
Waste Manag
January 2025
Chair of Waste Processing Technology and Waste Management, Montanuniversitaet Leoben, Leoben, Austria. Electronic address:
Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through intensified processes, increased efficiency, and enhanced yield.
View Article and Find Full Text PDFSpectrum Sensing in Very Low SNR Environment Using Multi-Scale Temporal Correlation Perception with Residual Attention.
Sensors (Basel)
January 2025
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales.
View Article and Find Full Text PDFCombining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines.
Sensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFLDDP-Net: A Lightweight Neural Network with Dual Decoding Paths for Defect Segmentation of LED Chips.
Sensors (Basel)
January 2025
Mechnical and Vehicle Engineering, Hunan University, Changsha 411082, China.
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss.
View Article and Find Full Text PDFA Fast Identification Method for Seismic Responses of Bridge Structures by Integrating Digital Signal Features and Deep Learning.
Sensors (Basel)
January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
A method of bridge structure seismic response identification combining signal processing technology and deep learning technology is proposed. The short-time energy method is used to intelligently extract the non-smooth segments in the sensor acquired signals, and the short-time Fourier transform, continuous wavelet transform, and Meier frequency cestrum coefficients are used to analyze the spectrum of the non-smooth segments of the response of the bridge structure, and the response feature matrix is extracted and used to classify sequences or images in the LSTM network and the Resnet50 network. The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!