We describe a required course for fourth-year medical students focusing on the application of the social sciences and the humanities to critical decisions in the practice of medicine. During 160 hours (70 with faculty contact) in a 7-week period, active, patient-centered, problem-based learning takes place in small collaborating groups, is facilitated by trained tutors, and uses computerized access to library materials plus reference files and resource persons. Major issues identified in the cases are clarified in complementary lectures and symposia. Formative evaluation is ongoing within tutorial groups. Summative evaluation is determined by the individual student's performance in a final complex management problem using a simulated patient. Evaluation of the course, and the basis for its ongoing revision, are provided by participating students and faculty, whose evaluations of the course have been favorable in 80% to 90% of cases.
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http://dx.doi.org/10.7326/0003-4819-116-7-569 | DOI Listing |
Acta Psychol (Amst)
January 2025
Language and Literature Department, Lorestan University, Iran.
Active Learning (AL) represents a transformative instructional approach that departs from traditional methods by immersing students in experiential learning activities such as problem-solving, discussions, role-plays, interactive engagement, and case studies. Despite its widely recognized potential, the effects of AL on psycho-affective constructs in English as a Foreign Language (EFL) contexts remain underexplored. Hence, this study explored the impact of AL on EFL learners' motivation, attitudes, and anxiety in Iran.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Department of CSE, Chandigarh Group of Colleges, Landran, Mohali, India.
The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes and generate inaccurate results since they are executed by human beings and cause delays in treatment that can be fatal. Considering these and other shortcomings there exists a need to have more efficient and accurate solutions based on artificial intelligence.
View Article and Find Full Text PDFPLoS One
January 2025
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.
View Article and Find Full Text PDFPLoS One
January 2025
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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