Many medical schools around the world have included professionalism training in their formal curriculum. However, these efforts may not be adequate; given the exposure of students to unprofessional behaviors in the clinical settings. In the present study, we aimed to design, implement, and evaluate a longitudinal program to improve professionalism among medical students upon their transition to clinical settings. A total of 75 medical students were enrolled in the study and randomly assigned to two groups. The control group did not receive any training, while for the intervention group; a 10-hour program through 16 weeks was organized based on the Holmes' reflection approach. The effectiveness of the program was evaluated by measuring three outcomes in both groups. Data analysis was performed using paired t-test and Multiple Linear Regression. Scores of judgment of professionalism increased in the intervention group (from 7.56 to 10.17; < 0.001), while there was no significant improvement in the control group's scores. Students' attitudes towards professionalism and their professional behaviors did not change significantly. Based on our findings, the Holmes reflection approach helps students improve their cognitive base of professionalism. Long-term follow-up and further qualitative studies will help us better understand the effects of this approach on other desirable outcomes.
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http://dx.doi.org/10.18502/jmehm.v13i12.4388 | DOI Listing |
Sci Rep
December 2024
Department of Mathematics, GC University, Lahore, Pakistan.
In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system's intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea.
In optical imaging of solid tumors, signal contrasts derived from inherent tissue temperature differences have been employed to distinguish tumor masses from surrounding tissue. Moreover, with the advancement of active infrared imaging, dynamic thermal characteristics in response to exogenous thermal modulation (heating and cooling) have been proposed as novel measures of tumor assessment. Contrast factors such as the average rate of temperature changes and thermal recovery time constants have been investigated through an active thermal modulation imaging approach, yielding promising tumor characterization results in a xenograft mouse model.
View Article and Find Full Text PDFJ Adv Nurs
December 2024
College of Nursing, Brigham Young University, Provo, Utah, USA.
Introduction: Phenomenology is essential for researchers exploring human experience. To apply it rigorously, an understanding of its philosophical foundations is needed. This discussion outlines the key distinctions between interpretive and descriptive phenomenology to illustrate philosophical and methodological implications.
View Article and Find Full Text PDFKidney Med
January 2025
Division of Rheumatology, Department of Medicine, University of California San Francisco, San Francisco, CA.
Rationale & Objective: Dialysis patient care technicians (PCTs) provide essential, frontline care for patients receiving in-center hemodialysis. We qualitatively explored perceptions of the PCT job role, responsibilities, and training among current PCTs, non-PCT dialysis staff, and patients receiving hemodialysis.
Study Design: Focus group study.
Front Plant Sci
December 2024
Institute of Biotechnology, Jiaxing Academy of Agricultural Science, Jiaxing, China.
Nitrogen is essential for rice growth and yield formation, but traditional methods for assessing nitrogen status are often labor-intensive and unreliable at high nitrogen levels due to saturation effects. This study evaluates the effectiveness of flavonoid content (Flav) and the Nitrogen Balance Index (NBI), measured using a Dualex sensor and combined with machine learning models, for precise nitrogen status estimation in rice. Field experiments involving 15 rice varieties under varying nitrogen application levels collected Dualex measurements of chlorophyll (Chl), Flav, and NBI from the top five leaves at key growth stages.
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