Background: The Clinical Learning Environment, Supervision and Nurse Teacher scale is a reliable and valid instrument to evaluate the quality of the clinical learning process in international nursing education contexts.
Objectives: This paper reports the development and psychometric testing of the Spanish version of the Clinical Learning Environment, Supervision and Nurse Teacher scale.
Design: Cross-sectional validation study of the scale.
Setting: 10 public and private hospitals in the Alicante area, and the Faculty of Health Sciences (University of Alicante, Spain).
Participants: 370 student nurses on clinical placement (January 2011-March 2012).
Methods: The Clinical Learning Environment, Supervision and Nurse Teacher scale was translated using the modified direct translation method. Statistical analyses were performed using PASW Statistics 18 and AMOS 18.0.0 software. A multivariate analysis was conducted in order to assess construct validity. Cronbach's alpha coefficient was used to evaluate instrument reliability.
Results: An exploratory factorial analysis identified the five dimensions from the original version, and explained 66.4% of the variance. Confirmatory factor analysis supported the factor structure of the Spanish version of the instrument. Cronbach's alpha coefficient for the scale was .95, ranging from .80 to .97 for the subscales.
Conclusion: This version of the Clinical Learning Environment, Supervision and Nurse Teacher scale instrument showed acceptable psychometric properties for use as an assessment scale in Spanish-speaking countries.
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http://dx.doi.org/10.1016/j.ijnurstu.2014.08.008 | DOI Listing |
HGG Adv
January 2025
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196).
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Faculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.
Purpose: Structured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.
Methods: In this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented.
Apoptosis
January 2025
Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Cancer-associated fibroblasts (CAFs) significantly influence tumor progression and therapeutic resistance in colorectal cancer (CRC). However, the distributions and functions of CAF subpopulations vary across the four consensus molecular subtypes (CMSs) of CRC. This study performed single-cell RNA and bulk RNA sequencing and revealed that myofibroblast-like CAFs (myCAFs), tumor-like CAFs (tCAFs), inflammatory CAFs (iCAFs), CXCL14CAFs, and MTCAFs are notably enriched in CMS4 compared with other CMSs of CRC.
View Article and Find Full Text PDFSci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
Genetics plays a significant role in Multiple Sclerosis (MS), with approximately 12.6% of cases occurring in familial form. While previous studies have demonstrated differences in disease progression and MRI findings between familial and sporadic MS, there has been no comparison of cognitive impairment between them.
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