Predicting Internet Addiction in Medical Students by General Self-efficacy, Difficulty in Emotion Regulation, and Resilience

The present study aimed to predict internet addiction based on general self-efficacy, difficulty in emotion regulation, and resilience in medical students.

This was a cross-sectional study. The statistical population included all medical students of Shahid Beheshti University of Medical Sciences. The research sample consisted of 96 medical students selected by random sampling method in 2018. Data collection was performed by Sherer General Self-Efficacy Scale, Gramat’s and Roemer’s Difficulties in Emotion Regulation Scale, Connor–Davidson Resilience Scale, and Young’s Internet Addiction test.

To analyze the obtained data, Pearson’s correlation coefficient and the stepwise regression model were used. The obtained results suggested a significant relationship between internet addiction and general self-efficacy, difficulty in emotion regulation, and resiliency (P<0.05). Additionally, general self-efficacy, difficulty in emotion regulation, and resilience are able to predict 27% of internet addiction variance in medical students.

To prevent and reduce the harm of internet addiction in students in stressful events, they should be trained to improve their resilience, self-efficacy, and emotion regulation skills.