华南预防医学 ›› 2026, Vol. 52 ›› Issue (1): 37-42.doi: 10.12183/j.scjpm.2026.0037

• 论著 • 上一篇    下一篇

高职学生网络成瘾的潜在剖面分析及影响因素研究

罗银霞1, 段卓2, 罗巧2, 顾杨2, 周航2, 毛孝容3   

  1. 1.达州中医药职业学院,四川 达州 635000;
    2.广安职业技术学院;
    3.四川省医学科学院*四川省人民医院(电子科技大学附属医院)
  • 收稿日期:2025-02-05 出版日期:2026-01-20 发布日期:2026-02-06
  • 通讯作者: 毛孝容,E-mail:xiaorong_mao@qq.com
  • 作者简介:罗银霞(1989—),女,硕士研究生,主管护师,从事护理教育、心理健康教育研究工作
  • 基金资助:
    四川省心理健康教育研究中心项目(XLJKJY2534B); 四川省广安市社会科学研究规划项目基金资助项目(2024YB038)

A latent profile analysis of internet addiction and its influencing factors among vocational college students

Luo Yinxia1, Duan Zhuo2, Luo Qiao2, Gu Yang2, Zhou Hang2, Mao Xiaorong3   

  1. 1. Dazhou Vocational College of Traditional Chinese Medicine, Dazhou, Sichuan 635000, China;
    2. Guang'an Vocational and Technical College;
    3. Sichuan Academy of Medical Sciences·Sichuan Provincial People's Hospital(Affiliated Hospital of University of Electronic Science and Technology of China)
  • Received:2025-02-05 Online:2026-01-20 Published:2026-02-06

摘要: 目的 探究高职学生网络成瘾的潜在剖面及影响因素,为构建差异化干预体系提供依据。方法 2024年6—10月便利选取四川省3所高职院校的学生为研究对象,使用一般人口学问卷、网络成瘾量表、心理生活质量评价问卷、一般自我效能感量表进行调查。对高职学生网络成瘾特征进行潜在剖面分析,采用多分类logistic回归分析检验影响因素。结果 回收有效问卷1 037份。高职学生网络成瘾分为无网络成瘾组(42.1%)、中度网络使用组(40.2%)、高风险网络成瘾组(17.7%)。多分类logistic回归分析结果显示,心理生活质量、一般自我效能感、运动频率、与父母沟通情况、是否担任班干部是高职学生网络成瘾分类的影响因素。心理生活质量得分高(中度网络使用组vs无网络成瘾组:OR=0.971,高风险网络成瘾组vs无网络成瘾组:OR=0.930)、一般自我效能感得分高(OR=0.133)、运动频率越高(OR=0.476)的高职学生更倾向于无网络成瘾组;与父母沟通较少(OR=1.887)、心理生活质量得分越低(OR=1.044)、一般自我效能感得分越低(OR=1.860)、担任班干部(OR=0.556)的高职学生更倾向于“高风险网络成瘾组”(均P<0.05)。结论 高职学生网络成瘾分为3个潜在剖面。管理者需依据不同群体特征,制定并实施针对性的干预策略,以有效预防和改善网络成瘾问题。

关键词: 高职学生, 网络成瘾, 心理生活质量, 潜在剖面分析, 影响因素

Abstract: Objective To investigate the latent profiles of internet addiction among vocational college students and identify its influencing factors, thereby providing a basis for the development of differentiated intervention strategies. Methods A cross-sectional study was conducted between June and October 2024, utilizing convenience sampling to recruit students from three vocational colleges in Sichuan Province. Data were collected through a general demographic questionnaire, the Internet Addiction Scale, the Psychological Quality of Life Assessment Questionnaire, and the General Self-Efficacy Scale. Latent Profile Analysis (LPA) was employed to identify distinct profiles of internet addiction, and multinomial logistic regression was used to examine the associated factors. Results A total of 1 037 valid questionnaires were analyzed. Three distinct profiles of internet addiction were identified: a "No Internet Addiction" group (42.1%), a "Moderate Internet Use" group (40.2%), and a "High-Risk Internet Addiction" group (17.7%). Multinomial logistic regression analysis indicated that psychological quality of life, general self-efficacy, frequency of physical exercise, communication with parents, and whether the student held a position as a class cadre were significant factors influencing the latent profile membership. Specifically, students with higher scores in psychological quality of life (Moderate Internet Use vs. No Internet Addiction: OR=0.971; High-Risk Internet Addiction vs. No Internet Addiction: OR=0.930), higher general self-efficacy (OR=0.133), and highter physical exercise frequency (OR=0.476) were more likely to be classified in the "No Internet Addiction" group. Conversely, students who communicated less frequently with their parents (OR=1.887), had lower psychological quality of life (OR=1.044), lower general self-efficacy (OR=1.860), and hold a class cadre position (OR=0.556) were more likely to belong to the "High-Risk Internet Addiction" group (all P<0.05). Conclusion Internet addiction among vocational college students manifests in three distinct latent profiles. It is imperative for administrators to formulate and implement targeted intervention strategies based on the specific characteristics of these different groups to effectively prevent and mitigate problematic internet use.

Key words: Vocational college students, Internet addiction, Psychological quality of life, Latent profile analysis, Influencing factors

中图分类号: 

  • R195.4