Social Innovation Through Conceptual Replication

Abstract

Social innovation can involve reexamining traditional perspectives from new angles. This study illustrates how replicating a published study can provide new insights into understanding symptoms of depression and anxiety among medical students. The primary aim of this study is to reanalyze the dataset published by Santander-Hernandez et al. (2022), employing data mining techniques to expand upon the findings of the original study. In Santander-Hernandez et al. (2022), a questionnaire was administered to 371 medical students in Peru covering various aspects, such as smartphone dependence, insomnia, depressive symptoms, anxiety symptoms, body mass index, suicidal ideation, and other demographic factors. The current study analyzed this dataset using a data mining approach, specifically a classification tree. The results of the data mining, with a prediction accuracy of 78.04%, indicate that insomnia, suicidal ideation, body mass index, and study year may be associated with symptoms of depression and anxiety.