Evaluation of Algorithmic Training Efficacy in Neurosurgical Emergencies for Medical Students

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 manner close to clinics. Medical students require novel educational approaches that will enable them to function efficiently in clinical conditions. In the present study, we evaluated 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, each comprising 30 interns as case and control groups, on a multiple-choice questionnaire with confirmed validity and reliability. The scores were compared before training and after two weeks of training, which was presented using the algorithmic method for the case group and lectures for the control group. Data were analyzed on SPSS software using independent t-test, paired t-test and ANOVA. Findings: In the case group, the mean scores of interns increased from 10.034 ± 1.56 before training to 15.23 ± 1.57 after algorithmic training, indicating a significant difference. In the control group, the mean scores of interns increased from 10.47 ± 2.43 before training to 12.33 ± 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. Conclusion: 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.

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