Aim: To use a concept analysis to determine a clear definition of the term "intentional learning" for use in nursing.
Background: The term intentional learning has been used for years in educational, business, and even nursing literature. It has been used to denote processes leading to higher order thinking and the ability to use knowledge in new situations; both of which are important skills to develop in nursing students. But the lack of a common, accepted definition of the term makes it difficult for nurse educators to base instruction and learning experiences on or to evaluate its overall effectiveness in educating students for diverse, fast-paced clinical practices.
Design And Review Methods: A concept analysis following the eight-step method developed by Walker and Avant (2011).
Data Sources: Empirical and descriptive literature.
Results: Five defining attributes were identified: (1) self-efficacy for learning, (2) active, effortful, and engaged learning, (3) mastery of goals where learning is the goal, (4) self-directed learning, and (5) self-regulation of learning.
Conclusion: Through this concept analysis, nursing will have a clear definition of intentional learning. This will enable nurse educators to generate, evaluate, and test learning experiences that promote further development of intentional learning in nursing students. Nurses in practice will also be able to evaluate if the stated benefits are demonstrated and how this impacts patient care and outcomes.
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http://dx.doi.org/10.1111/nuf.12222 | DOI Listing |
Comput Inform Nurs
January 2025
Author Affiliations: Division of Hematology and Medical Oncology, Department of Internal Medicine, Wan Fang Hospital (Dr Chang), Department of Internal Medicine, School of Medicine, College of Medicine (Dr Chang), School of Nursing, College of Nursing (Tsai, Dr Huang), and Department of Nursing (Tsai, Lu, Huang) and Research Center in Nursing Clinical Practice (Tsai, Dr Huang), Wan Fang Hospital, Department of Nursing (Chan), and Cochrane Taiwan, Taipei Medical University (Dr Huang), Taipei, Taiwan; Department of Nursing, Faculty of Medicine, Universitas Pendidikan Ganesha, Bali, Indonesia (Gautama).
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Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
In human activity-recognition scenarios, including head and entire body pose and orientations, recognizing the pose and direction of a pedestrian is considered a complex problem. A person may be traveling in one sideway while focusing his attention on another side. It is occasionally desirable to analyze such orientation estimates using computer-vision tools for automated analysis of pedestrian behavior and intention.
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December 2024
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300384, China.
Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation.
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NYU, New York, NY, USA.
Heavy drinking is a major public health concern, particularly among young adults who often experience fear of being stigmatized when seeking help for alcohol-related problems. To address drinking concerns outside clinical settings, we tested the feasibility of a novel imagery-based behavior change strategy led by student lay interventionists in a college setting. Participants were adults recruited on a college campus and were randomized to either learn the four steps of WOOP (Wish, Outcome, Obstacle, and Plan) or to learn a format-matched Sham WOOP (Wish, Outcome, "Outcome," and Plan).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
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Yale Jackson School of Global Affairs, Yale University, New Haven, CT 06511.
The recent COVID-19 pandemic offers a rare opportunity to understand how citizens attribute responsibility for governments' responses to unanticipated negative-and in this case, systemic-exogenous shocks. Classical accounts of responsibility are complicated when crises are pervasive, involve multiple valence dimensions, and where individuals can make relative assessments of performance. We fielded a conjoint experiment in 16 countries with 22,147 respondents.
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