Introduction: Team-based learning (TBL) is an active learning strategy that gives students the opportunity to apply conceptual information through a series of tasks that incorporate individual effort, team collaboration, and immediate feedback. This study aimed to report baseline TBL implementation in a clinical module of a fourth-year competency-based undergraduate anesthesia curriculum and explore the perspectives of students.
Methods: In April 2023, 18 students participated in two TBL sessions over two weeks, and readiness assurance test results and post-TBL evaluations were analyzed. Week one TBL implementation scores were compared with week two, establishing a longitudinal analysis over two points in time. Students also participated in an online survey to assess their views on the advantages and design of TBL, their perceptions of its best and worst features, and their suggestions for its implementation.
Results: Of 18 students, 16 (89%) responded to the survey. Most students believed that TBL was an effective educational strategy but expressed concern about the amount of time required for TBL preparation and the need for student readiness. The individual readiness assurance test scores did not differ significantly between weeks 1 and 2 (mean difference [MD] = 0.39, P= 0.519, 95% CI: -0.824 to 1.60). However, the students' median [IQR] team readiness assurance test scores increased significantly from week one to week two, from 8 [2] to 10 [1] (p = 0.004). Peer evaluation scores also showed a significant increase in week 2 (MD = 2.4, P = 0.001, 95% CI: -3.760 to -0.996).
Conclusion: TBL was successfully implemented for a clinical module at Dilla University-Ethiopia for the first time. Students perceived it positively, but some criticized its preparation time, workload, and minimal facilitator engagement. We suggest convenient and flexible scheduling personalized for each student's needs when TBL is applied for clinical modules.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10729834 | PMC |
http://dx.doi.org/10.2147/AMEP.S437710 | DOI Listing |
Front Cell Infect Microbiol
January 2025
Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
Introduction: This study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosis.
Methods: Proteomic analysis was conducted on serum samples from patients with acute and chronic brucellosis, as well as from healthy controls. Differential expression analysis was performed to identify proteins with altered expression, while Weighted Gene Co-expression Network Analysis (WGCNA) was applied to detect co-expression modules associated with clinical features of brucellosis.
Front Immunol
January 2025
Department of Gynecology, Handan Central Hospital, Handan, China.
Background: Ferroptosis, a recently discovered iron-dependent cell death, is linked to various diseases but its role in endometriosis is still not fully understood.
Methods: In this study, we integrated microarray data of endometriosis from the GEO database and ferroptosis-related genes (FRGs) from the FerrDb database to further investigate the regulation of ferroptosis in endometriosis and its impact on the immune microenvironment. WGCNA identified ferroptosis-related modules, annotated by GO & KEGG.
Life Med
October 2024
State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China.
Recurrent implantation failure (RIF) is a leading impediment to assisted reproductive technology, yet the underlying pathogenesis of RIF remains elusive. Recent studies have sought to uncover novel biomarkers and etiological factors of RIF by profiling transcriptomes of endometrial samples. Nonetheless, the inherent heterogeneity among published studies and a scarcity of experimental validations hinder the identification of robust markers of RIF.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Background: The tumor microenvironment (TME) is integral to tumor progression. However, its prognostic implications and underlying mechanisms in clear cell renal cell carcinoma (ccRCC) are not yet fully elucidated. This study aims to examine the prognostic significance of genes associated with immune-stromal scores and to explore their underlying mechanisms in ccRCC.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Background: Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. However, existing approaches primarily focus on spatial domain information, neglecting the periodic information in the frequency domain and the complementary relationship between the two domains. In this paper, we proposed a generative adversarial network that employs a cross-attention mechanism to extract and fuse features across spatial and frequency domains.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!