No previous studies have evaluated arts based recovery college courses. Yet arts may assist in personal recovery, as often defined by service users, through social connection and personal meaning. This interdisciplinary study evaluated (i) whether self-reported wellbeing and arts activities increased following arts based recovery college courses, and (ii) how students, peer trainers and artist-trainers understood courses' impact. The design was mixed-methods. Of 42 service user students enrolling, 39 completed a course and 37 consented to provide data. Of these, 14 completed pre and post course questionnaires on mental wellbeing and 28 on arts participation. Post course focus groups were held with six of eight peer trainers and five of seven artist-trainers, and 28 students gave written feedback. Twenty-four students were interviewed up to three times in the subsequent nine months. There were statistically significant increases in self-reported mental wellbeing and range of arts activities following course attendance. At follow-up 17 of 24 students reported improved mental wellbeing, while seven reported little or no change. Some spoke of increased social inclusion and continuing to use skills learned in the course to maintain wellbeing. Initial in-course experience of 'artistic growth' predicted follow-up reports of improvement. Future controlled studies should employ standardized measures of social inclusion and arts participation.
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http://dx.doi.org/10.3390/ijerph15061170 | DOI Listing |
BMC Public Health
January 2025
Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Dokuz Eylul University, İzmir, Türkiye.
Background: MPOX (Monkeypox) is a zoonotic disease of increasing global concern due to its re-emergence and potential for human-to-human transmission. Effective public health interventions rely on understanding socio-demographic determinants of knowledge and perceptions of the disease. This study aimed to investigate MPOX-related knowledge and concerns among a diverse sample in Türkiye, identifying key factors influencing knowledge levels.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Health Management of Public Health, College of Public Health, Zhengzhou University, 100 Kexue Road, Gaoxin district, Zhengzhou, Henan, 450001, China.
Background: Although China has implemented multiple policies to encourage childbirth, the results have been underwhelming. Migrant workers account for a considerable proportion of China's population, most of whom are of childbearing age. However, few articles focus on their fertility intentions.
View Article and Find Full Text PDFCommun Biol
January 2025
University of Twente, Enschede, The Netherlands.
Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. Prevailing detection methods treat the design of the classifier as a discrete phase, assuming that high classification accuracy is the sole prerequisite for precise detection. This frequently steers method development toward classification-driven optimizations that can inadvertently impede detection.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Rehabilitation Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues for contour distinction, which has a high demand for the network architecture design and its practical training status. To this end, we design auxiliary and refined constraints to optimize the energy function by supplying additional guidance in training procedure, thus promoting model's ability to capture information.
View Article and Find Full Text PDFSci Rep
January 2025
Center for Integrated Quantum Science and Technology (IQST), University of Stuttgart, 70569, Stuttgart, Germany.
Highly entangled quantum states are an ingredient in numerous applications in quantum computing. However, preparing these highly entangled quantum states on currently available quantum computers at high fidelity is limited by ubiquitous errors. Besides improving the underlying technology of a quantum computer, the scale and fidelity of these entangled states in near-term quantum computers can be improved by specialized compilation methods.
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