Objective: To explore factors influencing the expansion of the peer-based technologist Coaching Model Program (CMP) from its origins in mammography and ultrasound to all imaging modalities at a single tertiary academic medical center.
Methods: After success in mammography and ultrasound, efforts to expand the CMP across all Stanford Radiology modalities commenced in September 2020. From February to April 2021 as lead coaches piloted the program in these novel modalities, an implementation science team designed and conducted semistructured stakeholder interviews and took observational notes at learning collaborative meetings. Data were analyzed using inductive-deductive approaches informed by two implementation science frameworks.
Results: Twenty-seven interviews were collected across modalities with radiologists (n = 5), managers (n = 6), coaches (n = 11), and technologists (n = 5) and analyzed with observational notes from six learning meetings with 25 to 40 recurrent participants. The number of technologists, the complexity of examinations, or the existence of standardized auditing criteria for each modality influenced CMP adaptations. Facilitators underlying program expansion included cross-modality learning collaborative, thoughtful pairing of coach and technologist, flexibility in feedback frequency and format, radiologist engagement, and staged rollout. Barriers included lack of protected coaching time, lack of pre-existing audit criteria for some modalities, and the need for privacy of auditing and feedback data.
Discussion: Adaptations to each radiology modality and communication of these learnings were key to disseminating the existing CMP to new modalities across the entire department. An intermodality learning collaborative can facilitate the dissemination of evidence-based practices across modalities.
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http://dx.doi.org/10.1016/j.jacr.2022.10.007 | DOI Listing |
Intern Emerg Med
January 2025
Department of Renal Medicine, Northern Care Alliance, Salford Royal Hospital, Salford, M6 8HD, UK.
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Department of Psychology, University of Virginia, Charlottesville, VA, 22904, USA.
This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal's evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal's influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics.
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January 2025
Research group, FAITH research, Leeuwarden, Groningen, The Netherlands.
The growing complexity of care and healthcare workforce shortages in the Netherlands necessitates exploring interprofessional collaboration (IPC). However, the predominant single-professional education may result in a professional identity (PI) among healthcare students, which may not support successful IPC. Internships in student-run interprofessional learning wards (SR-IPLW) could foster interprofessional identity (IPI) development.
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January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
View Article and Find Full Text PDFNanoscale Horiz
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SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, Southeast University, Nanjing 210096, China.
Janus MoSiGeN monolayers exhibit exceptional mechanical stability and high electron mobility, which make them a promising channel candidate for field-effect transistors (FETs). However, the high Schottky barrier at the contact interface would limit the carrier injection efficiency and degrade device performance. Herein, using density functional theory calculations and machine learning methods, we investigated the interfacial properties of the Janus MoSiGeN monolayer and metal electrode contacts.
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