Background: Embedding research into practice is challenging. Barriers include: a shortage of time, lack of understanding of the evidence and a poor support in the clinical setting. A community of practice (CoP) model has been used to address these issues. Three 'Evidence into Practice' groups use a CoP model to assist the rapid translation of evidence into practice in primary and secondary care settings. We describe how a CoP model supports the functions, operations and outputs of three 'Evidence into Practice Groups'.
Method: A CoP model is used to engage a broad range of clinicians, researchers, managers, patients and librarians in the complex process of acquiring research knowledge and then translating knowledge into practice. The CoP principles of Domain, Community and Practice are used to describe three 'Evidence into Practice Groups' who cater for different elements of the care and academic sector and engage a range of professional groups. This includes primary and secondary care engaging professionals such as general practitioners (GP), practice nurses, allied health professionals, researchers and librarians. All groups are clinically led, academically supported and follow similar processes to identify the best evidence and translate it into practice. As the groups reflect the context in which they work they have different operational arrangements for example frequency and time of meetings.
Results: The CoP model enabled three 'Evidence into Practice Groups' over time to: engage over 180 clinical and academic staff; answer 130 clinical questions; improve clinical care, gain funding for two randomised controlled trials (enrolled over n = 7000 participants) and identify areas for further research, quality improvement audit and training.
Conclusion: The CoP model encourages the rapid translation of evidence into practice by engaging staff to identify areas of clinical concern in their own context, thereby stimulating their interest and involvement. This creates a meaningful link between research and practice. Clinical leadership and the CoP model ensure that practice change is quick and efficient. This model can be replicated at scale. Consideration needs to be given to the key ingredients to achieve impact.
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Sci Rep
December 2024
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, School of Computer and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability.
View Article and Find Full Text PDFSci Rep
December 2024
Key Laboratory of Exercise and Physical Fitness, Ministry of Education, Beijing Sport University, Beijing, China.
Chronic sedentary behavior can have a negative impact on the executive function (EF) of young people. While physical activity (PA) has been shown to improve this phenomenon, the effects of different types of PA on EF vary. In this study, we compared the effects of moderate-intensity continuous training (MICT) (60-70% HRmax, 30 min), body weight training (BWT) (2 sets tabata, 20 min), and mind-body exercise (MBE) (2 sets Yang style shadowboxing, 20 min) on EF in 59 sedentary youth (n = 59, age = 20.
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December 2024
National Health Commission Key Laboratory of Parasitic Diseases Prevention and Control, Wuxi 214064, China.
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View Article and Find Full Text PDFClin Teach
February 2025
Irish College of GPs, Dublin, Ireland.
Peer review is the cornerstone of academic publishing that upholds the quality and integrity of scholarly work. However, there is an ever-growing struggle to recruit peer reviewers, termed the 'peer review crisis', driven by a shrinking academic workforce and increased workload. Additionally, there is a notable lack of standardised training for peer reviewers which poses a challenge in maintaining high-quality reviews.
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Norwegian Police IT-unit, Fridtjof Nansens vei 14, 0031 Oslo, Norway.
Law enforcement agencies manually transcribe thousands of investigative interviews per year in relation to different crimes. In order to automate and improve efficiency in the transcription of such interviews, applied research explores artificial intelligence models, including Automatic Speech Recognition (ASR) and Natural Language Processing. While AI models can improve efficiency in criminal investigations, their successful implementation requires evaluation of legal and technical risks.
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