خلاصة:
Objective: The present study aimed to predict internet addiction based on general self-efficacy,
difficulty in emotion regulation, and resilience in medical students.
Methods: 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.
Results: 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.
Conclusion: 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
ملخص الجهاز:
Research Paper: Predicting Internet Addiction in Medical Students by General Self-efficacy, Difficulty in Emotion Regulation, and Resilience Leila Salek Ebrahimi1 /, Gholamreza Ahmadi2 , Abbas Masjedi Arani3, Seyedeh Elnaz Mousavi4* / 1.
Predicting Internet Addiction in Medi-cal Students by General Self-efficacy, Difficulty in Emotion Regulation, and Resilience.
The obtained results suggested a significant relationship between internet addiction and general self-efficacy, difficulty in emotion regulation, and resiliency (P .
com 167 July 2019, Volume 7, Number 3 Highlights • The obtained results indicate a significant relationship between internet addiction and general self-efficacy, difficulty in emotion regulation, and resilience.
• General self-efficacy, difficulty in emotion regulation, and resilience can predict internet addiction in medical students.
According to various studies, there is a significant relationship between IA and pathological factors, such as anxiety, Obsessive-Compulsive Disorder (OCD), depression, substance, alcohol and tobacco use, Atten-tion-Deficit/Hyperactivity Disorder (ADHD), sleep-ing problems, and high-risk sexual behaviors (Koukia, Mangoulia, Alexiou, 2014; Sung, Lee, Noh, Park, Ahn, 2013; Ghanbari, Amani, Namdari Pezhman, Bidi, Kareshki, 2012; Mazhari, 2012; Alavi, Maracy, Jannati-fard, Eslami, Haghighi, 2010; Jafari Fatehizadeh, 2012; Khoshakhlagh Faramarzi, 2012; Ko, Yen, Yen, Chen, Chen, 2012).
In addition to the relationship between resilience and IA (Zhou, Zhang, Liu, Wang, 2017), reports indicated an association between resilience and emotion regulation (Andamikhoshk, Golzar, Esmaeilinasab, 2013), and self-efficacy (Craparo et al.
The obtained results suggested that although IA is less prevalent among medical students, it can be influenced by factors like emotion regulation strategies and self-efficacy.