Abstract
BACKGROUND: Nowadays, the question of separation between training and clinics in many clinical fields is extensively debated, and all universities endeavor to present their theoretical education in a close connection with the clinics’ settings. Medical students require novel educational approaches that will enable them to have an efficient performance in clinical settings. The present study was aimed at evaluating the efficacy of algorithmic and lecture-based training on learning of interns. METHODS: In this experimental study, we assessed scores obtained by two groups of interns (case and control groups) that each group consisted of 30 interns as, on a multiple-choice questionnaire basis with high level of validity and reliability. The scores of interns for prior the training were compared with those scores obtained for two weeks post-training. The scores for case and control group were presented using the algorithmic and lecture-based method, respectively. Data were analyzed using the SPSS software (version 15). The independent t-test, paired t-test and ANOVA assessments were used for analyses. RESULTS: In the case group, the mean scores of interns increased from 10.034 (SD= 1.56) prior the training to 15.23 (SD=1.57) after algorithmic training, indicating a significant difference. In the control group, the mean scores of interns increased from 10.47 (SD=2.43) before training to 12.33 (SD=1.54) after lecture-based training, indicating a significant difference. Analysis of variance indicated that the mean score of interns after training in the case group was significantly higher, compared to those of the control group. CONCLUSIONS: Our findings demonstrate that training improves learning, and as medical students are more active in clinical fields, using novel methods of education such as algorithmic training may be more efficient compared to other methods. KEYWORDS: Training, Algorithmic Training, Medical Students, Medicine