An Evaluation of Curriculum change in Physiology: A Mixed method research design

Abstract

Introduction: The quality of both teaching and learning techniques in health sciences determines the competency of the doctors produced and the patient care. Realizing the necessity of active learning at the undergraduate level, curricular reforms were crucial to ensure that students play an active role in the learning process and imbibe all the prerequisite qualities of a competent health professional. The objective of this study was to implement and evaluate case-based learning in the physiology curriculum. Methods: The study included 150 first-year MBBS students. We followed a mixed research design in this study. A short lecture on anemia was followed by two sessions of CBL with a gap of one week. A structured questionnaire on a 5-point Likert scale was used to collect students perceptions. The internal consistency of the questionnaire was Cronbach alpha statistic = 0.8. Faculties feedback was collected through Focus Group Discussion (FGD). Results: Out of 145 participants, 117 responded to feedback. Students perceived that the CBL method was effective in understanding anemia topic (71%); promoted meaningful learning (83%); encouraged teamwork (69%); improved communication skills (65%); helps in future application of knowledge (81%); motivated self-directed learning (66%); helped to understand physiology concepts better (72%); leads to the development of problem-solving abilities (70%); and better student-teacher relationship (72%). Faculties suggested developing an assessment plan for future CBL sessions. Conclusion: In our experience, CBL is an effective, active teaching-learning tool that improves students understanding of basic concepts, clinical knowledge, problem-solving abilities, teamwork, communication skills, student-teacher relationship, and self-directed learning.

Gaussian field-based 3D-QSAR and molecular simulation studies to design potent pyrimidine–sulfonamide hybrids as selective BRAFV600Einhibitors

“…To evaluate the predictive ability of the QSAR models developed, pIC 50 values as dependent variables and Gaussian intensities as descriptors (independent variables) were used. 60,63…”