Context: teaching students in reflection calls for specific teacher competencies. We developed and validated a rating scale focusing on Student perceptions of their Teachers' competencies to Encourage Reflective Learning in small Groups (STERLinG).
Methods: we applied an iterative procedure to reduce an initial list of 241 items pertaining to teacher competencies to 47 items. Subsequently, we validated the instrument in two successive studies. In the first study, we invited 679 medical and speech and language therapy students to assess the teachers of their professional development groups with the STERLinG. Principal components analysis (PCA) with varimax rotation was used to investigate the internal structure of the instrument. In the second study, which involved 791 medical, dental, and speech and language therapy students, we performed a confirmatory factor analysis using the oblique multiple group (OMG) method to verify the original structure.
Results: in Study 1, 463 students (68%) completed the STERLinG. The PCA yielded three components: Supporting self-insight; Creating a safe environment, and Encouraging self-regulation. The final 36-item instrument explained 44.3% of the variance and displayed high reliability with α-values of 0.95 for the scale, and 0.91, 0.86 and 0.86 for the respective subscales. In Study 2, 501 students (63%) completed the STERLinG. The OMG confirmed the original structure of the STERLinG and explained 53.0% of the total variance with high α-values of 0.96 for the scale, and 0.94, 0.90 and 0.90 for the respective subscales.
Conclusions: the STERLinG is a practical and valid tool for gathering student perceptions of their teachers' competencies to facilitate reflective learning in small groups considering its stable structure, the correspondence of the STERLinG structure with educational theories and the coverage of important domains of reflection. In addition, our study may provide a theoretical framework for the practice of and research into reflective learning.
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http://dx.doi.org/10.1111/j.1365-2923.2010.03774.x | DOI Listing |
NPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand.
Active transportation, such as cycling, improves mobility and general health. However, statistics reveal that in low- and middle-income countries, male and female cycling participation rates differ significantly. Existing literature highlights that women's willingness to use bicycles is significantly influenced by their perception of security.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Division of Learning and Teaching, Charles Sturt University, Bathurst, NSW, Australia.
Background: Interviewers' judgements play a critical role in competency-based assessments for selection such as the multiple-mini-interview (MMI). Much of the published research focuses on the psychometrics of selection and the impact of rater subjectivity. Within the context of selecting for entry into specialty postgraduate training, we used an interpretivist and socio-constructivist approach to explore how and why interviewers make judgments in high stakes selection settings whilst taking part in an MMI.
View Article and Find Full Text PDFCurr Oncol Rep
January 2025
Department of Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa City, Chiba, Japan.
Purpose Of Review: Human epidermal growth factor receptor 2 (HER2) is a critical target in advanced gastric cancer (AGC). This review highlights the current treatment landscape, lessons learned from past clinical trials, and prospects for future treatment strategies for HER2-positive AGC.
Recent Findings: Trastuzumab had been the standard treatment for HER2-positive AGC for a decade, and subsequently, trastuzumab deruxtecan, an antibody-drug conjugate (ADC), emerged with an impressive response.
Ann Surg Oncol
January 2025
Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China.
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