Department of Community Medicine, Dr Baba Saheb Ambedkar Medical College and Hospital, Sector-6 Rohini, New Delhi, 110085, India.
BMC Psychiatry. 2021 Oct 15;21(1):511. doi: 10.1186/s12888-021-03529-z.
Globally, due to population diversity, the prevalence of problematic internet use (PIU) varies from 7.3 to 51%. This study aims to assess correlates of problematic internet use among undergraduate medical students of Delhi and derive a model for allocating new subjects among categories of internet users.
A cross-sectional study was conducted on 201 medical-undergraduate students in a medical college of Delhi from April 1st to May 31st, 2019. A semi-structured and pre-tested questionnaire was used to collect demographic information and factors affecting PIU. Dr. Kimberly Young's Internet Addiction Test (IAT) tool was used to assess PIU. Binary logistic regression has been applied to assess the correlates of PIU, and step-wise discriminant analysis (DA) has been applied to derive a model for allocation of new subjects among categories of internet users. Statistical Package for Social Sciences (Trial version 27.0; SPSS Inc., Chicago, IL) software was used for statistical analysis.
Total 41.3% of the subjects had PIU. Univariate analysis shows that internet use for emotional support, watching adult content, and gambling were significantly associated with PIU; however, in binary logistic regression, chatting, emotional support and watching online adult content were significant risk factors for PIU. The discriminant model correctly classified 66.2% of respondents into average and problematic internet user groups.
We should create awareness among medical students regarding problematic internet use and its potential harms; this could be included in the foundation course of curriculum implementation support program (CISP) for MBBS students.
在全球范围内，由于人口多样性，问题性互联网使用（PIU）的流行率在 7.3%至 51%之间不等。本研究旨在评估德里医学生中 PIU 的相关因素，并为在互联网用户类别中分配新对象建立模型。
在 2019 年 4 月 1 日至 5 月 31 日期间，在德里的一所医学院对 201 名医学生进行了横断面研究。使用半结构化和预测试问卷收集人口统计学信息和影响 PIU 的因素。使用 Kimberly Young 博士的互联网成瘾测试（IAT）工具评估 PIU。应用二元逻辑回归评估 PIU 的相关因素，并应用逐步判别分析（DA）为在互联网用户类别中分配新对象建立模型。使用统计软件包（社会科学试用版 27.0；SPSS Inc.，芝加哥，IL）进行统计分析。
总共有 41.3%的研究对象存在 PIU。单因素分析显示，使用互联网寻求情感支持、观看成人内容和赌博与 PIU 显著相关；然而，在二元逻辑回归中，聊天、情感支持和观看在线成人内容是 PIU 的显著危险因素。判别模型正确地将 66.2%的受访者分类为普通和问题性互联网用户群体。
我们应该提高医学生对问题性互联网使用及其潜在危害的认识；这可以包含在 MBBS 学生课程实施支持计划（CISP）的基础课程中。
