Aim: To evaluate the effects of a self-care promoting problem-based learning programme for people with rheumatic diseases in terms of health-related quality of life, empowerment, and self-care ability.
Background: Individuals with rheumatoid arthritis express a great need for education and support in adapting to the disease, but the average qualities of studies about patient education interventions are not high. There is no evidence of long-term benefits of patient education.
Design: Randomized controlled trial.
Methods: A randomized controlled design was selected with test at baseline, 1-week and 6-month post-interventions after completed the 1-year programme. The tests consisted of validity and reliability tested instruments. The participants were randomly assigned in spring 2009 to either the experimental group (n = 54) or the control group (n = 148). The programme was running alongside the standard care the participants received at a rheumatology unit. Parametric and non-parametric tests were used in the analyses.
Results: The participants in the experimental group had statistically significant stronger empowerment after participation in the self-care promoting problem-based learning programme compared with the control group, at the 6-month post-intervention. Approximately, two-thirds of the participants in the experimental group stated that they had implemented lifestyle changes due to the programme.
Conclusion: The self-care promoting problem-based learning programme enabled people with rheumatic diseases to improve their empowerment compared with the control group. It is important to continue to develop problem-based learning in patient education to find the very best way to use this pedagogical method in rheumatology care.
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http://dx.doi.org/10.1111/jan.12008 | DOI Listing |
The Problem: People use social media platforms to chat, search, and share information, express their opinions, and connect with others. But these platforms also facilitate the posting of divisive, harmful, and hateful messages, targeting groups and individuals, based on their race, religion, gender, sexual orientation, or political views. Hate content is not only a problem on the Internet, but also on traditional media, especially in places where the Internet is not widely available or in rural areas.
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Department of CSE, Chandigarh Group of Colleges, Landran, Mohali, India.
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Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Diriyah, Riyadh, Saudi Arabia.
Reinforcement learning is a remarkable aspect of the artificial intelligence field with many applications. Reinforcement learning facilitates learning new tasks based on action and reward principles. Motion planning addresses the navigation problem for robots.
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College of Landscape Architecture and Art, Jiangxi Agricultural University, Nanchang, China.
With the rapid development of artificial intelligence technology, an increasing number of village-related modeling problems have been addressed. However, first, the exploration of village-related watershed fine-grained classification problems, particularly the multi-view watershed fine-grained classification problem, has been hindered by dataset collection limitations; Second, village-related modeling networks typically employ convolutional modules for attentional modeling to extract salient features, yet they lack global attentional feature modeling capabilities; Lastly, the extensive number of parameters and significant computational demands render village-related watershed fine-grained classification networks infeasible for end-device deployment. To tackle these challenges, we introduce a multi-view attention mechanism designed for precise watershed classification, leveraging knowledge distillation techniques, abbreviated as MANet-KD.
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Geneis Beijing Co., Ltd, Beijing 100102, China.
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