Objectives: To explore the perspectives of healthcare professionals and commissioners working with a stroke Early Supported Discharge service in relation to: (1) the factors that facilitate or impede the implementation of the service, and (2) the impact of the service.
Design: Cross-sectional qualitative study using semi-structured interviews. Data were analysed by two researchers using a thematic analysis approach.
Setting: Two Early Supported Discharge services in Nottinghamshire.
Participants: Purposive sampling identified 35 key informants including practitioners, managers and commissioners.
Results: The identified facilitators to the implementation of evidence-based services were: (1) the adaptability of the intervention to the healthcare context, (2) the role of rehabilitation assistants and (3) cross-service working arrangements. Perceived challenges included: (1) lack of clarity regarding the referral decision making process, (2) delays in securing social care input and (3) lack of appropriate follow-on services in the region. Most respondents perceived the impact of the services to be: (1) reducing in-hospital stay, (2) aiding the seamless transfer of care from hospital to the community and (3) providing intensive stroke specific therapy. Commissioners called for greater evidence of service impact and clarity regarding where it fits into the stroke pathway.
Conclusions: Early Supported Discharge services were perceived as successful in providing home-based, stroke specific rehabilitation. Teams would benefit from capitalising on identified facilitators and developing strategies to address the challenges. The remit and impact of the services should be clear and demonstrable, with teams strengthening links with other health and social care providers.
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http://dx.doi.org/10.1177/0269215513502212 | DOI Listing |
J Eval Clin Pract
February 2025
Department of Midwifery, Faculty of Health Sciences, Biruni University, Istanbul, Türkiye.
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School of Marxism, China University of Political Science and Law (CUPL), Beijing, 100091, China.
To improve students' understanding of physical education teaching concepts and help teachers analyze students' cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students' long-term learning sequences and identify students' cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.
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