Online social networking (OSN) provides a new way for health professionals to communicate, collaborate and share ideas with each other for informal learning on a massive scale. It has important implications for ongoing efforts to support Continuing Professional Development (CPD) in the health professions. However, the challenge of analysing the data generated in OSNs makes it difficult to understand whether and how they are useful for CPD. This paper presents a conceptual model for using mixed methods to study data from OSNs to examine the efficacy of OSN in supporting informal learning of health professionals. It is expected that using this model with the dataset generated in OSNs for informal learning will produce new and important insights into how well this innovation in CPD is serving professionals and the healthcare system.
Download full-text PDF |
Source |
---|
J Med Internet Res
January 2025
Learning and Capacity Development Unit, Health Emergencies Programme, World Health Organization, Geneva, Switzerland.
Background: The COVID-19 pandemic demonstrated the global need for accessible content to rapidly train health care workers during health emergencies. The massive open access online course (MOOC) format is a broadly embraced strategy for widespread dissemination of trainings. Yet, barriers associated with technology access, language, and cultural context limit the use of MOOCs, particularly in lower-resource communities.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
School of Information and Artificial Intelligence, Anhui Provincial Engineering Research Center for Beidou Precision Agriculture Information, Key Laboratory of Agricultural Sensors for Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei, Anhui 230036, China.
Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years.
View Article and Find Full Text PDFEsophagus
January 2025
Department of Surgery, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
Background: Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the response to chemotherapy before treatment is desirable. However, there is currently no established method for predicting response to neoadjuvant chemotherapy.
View Article and Find Full Text PDFMol Divers
January 2025
Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases Ministry of Education, Jiangxi Province Key Laboratory of Biomaterials and Biofabrication for Tissue Engineering, Gannan Medical University, Ganzhou, 341000, Jiangxi, China.
Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Landscape Architecture, Remote Sensing and GIS Laboratory, University of Cukurova, Adana, 01330, Turkey.
Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!