Relationship among cyberbullying, depressive symptoms and suicidal ideation in college students
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摘要:
目的 识别大学生网络欺凌的潜在类别, 分析不同潜在类别与抑郁症状、自杀意念的关联, 以期为有效开展网络欺凌干预工作提供理论依据。 方法 采用方便整群随机抽样的方法, 于2021年4-5月在辽宁省和吉林省抽取1 094名大学生进行问卷调查。采用受网络欺凌行为问卷、流调中心抑郁量表中文版和Beck自杀意念量表中文版, 对数据进行潜在类别分析、单因素方差分析及Logistic回归分析。 结果 大学生网络欺凌可分为低发组(72.40%)、普通组(20.20%)和高发组(7.40%)3个类别, 不同类别的网络欺凌者在抑郁症状和自杀意念上差异均有统计学意义(P值均 < 0.01)。Logistic回归分析结果显示, 控制人口学变量后, 网络欺凌普通组和网络欺凌高发组均能正向预测抑郁症状(β普通组=0.88, β高发组=2.44)和自杀意念(β普通组=0.50, β高发组=1.20)(P值均 < 0.01)。 结论 家庭-学校-社会可以针对不同类别的网络欺凌大学生进行干预, 以防范大学生抑郁症状及自杀意念的发生, 提升其心理健康水平。 Abstract:Objective To identify the latent classes of cyberbullying in college students, and to analyze its relationship with depressive symptoms and suicidal ideation, so as to provide a theoretical reference for effectively intervention of cyberbullying. Methods Cluster random sampling was used to select 1 094 college students from Liaoning Province and Jilin Province from April to May, 2021.The Cyberbullying Victims'Behaviors Scale, the Center for Epidemiological Studies Depression Scale and BSI-CV were used in this study and data was analyzed by using latent class analysis, one-way analysis of variance and Logistic regression to analyze the data. Results Cyberbullying of college students could be divided into three latent classes: low incidence group (72.40%), general incidence group (20.20%) and high incidence group (7.40%).There were significant differences in depressive symptoms and suicidal ideation among different classes of cyberbullying (P < 0.01).Logistic regression analysis showed that after controlling for demographic variables, general incidence group and high incidence group significantly and positively predicted depressive symptoms (βgeneral incidence group=0.88, βhigh incidence group=2.44, P < 0.01) and suicidal ideation (βgeneral incidence group=0.50, βhigh incidence group=1.20, P < 0.01). Conclusion Families, schools and society should conduct different interventions for college students who suffer from different classes of cyberbullying to reduce depressive symptoms and suicidal ideation and promote mental health of college students. -
Key words:
- Internet /
- Violence /
- Depression /
- Suicide /
- Students
1) 利益冲突声明 所有作者声明无利益冲突。2) 王苗苗 -
表 1 大学生网络欺凌的潜在类别分析拟合信息(n=1 094)
Table 1. Latent class analysis and fit information of cyberbullying among college students(n=1 094)
模型 K Log(L) AIC值 BIC值 aBIC值 Entropy值 PLMRT值 PBLRT值 类别概率 1 24 -15 608.81 31 265.62 31 385.56 31 309.33 - - - 1.00 2 37 -13 192.62 26 459.24 26 644.15 26 526.63 0.97 0.00 0.00 0.78/0.22 3 50 -12 469.97 25 039.94 25 289.82 25 131.01 0.97 0.00 0.00 0.72/0.20/0.07 4 63 -11 975.02 24 076.04 24 390.89 24 190.79 0.97 0.08 0.00 0.73/0.12/0.09/0.06 表 2 不同组别大学生受网络欺凌行为维度得分比较/(x±s)
Table 2. Scores of cyberbullying dimensions in different classes among college students/(x±s)
组别 人数 言语欺凌 隐匿身份 伪造欺诈 低发组 792 5.65±1.05 2.77±1.06 6.06±1.56 普通组 221 10.73±2.02 4.04±1.42 8.56±2.51 高发组 81 14.83±2.87 6.10±1.43 14.25±3.27 Welch值 1 025.07** 263.19** 331.53** 事后检验 C3>C2>C1 C3>C2>C1 C3>C2>C1 注: **P < 0.01;C1代表低发组,C2代表普通组,C3代表高发组。 表 3 不同网络欺凌类别的大学生抑郁症状和自杀意念得分比较/(x±s)
Table 3. Comparison of differences in depressive symptoms and suicidal ideation among college students under different classes of cyberbullying/(x±s)
组别 人数 抑郁情绪 积极情绪 躯体症状与活动迟滞 人际关系 抑郁症状 自杀意念 低发组 792 11.34±4.02 12.42±3.35 9.18±2.89 2.44±0.93 35.38±8.51 0.99±1.68 普通组 221 13.36±4.60 13.37±3.15 10.42±3.01 2.89±1.28 40.04±9.66 1.60±2.12 高发组 81 15.89±5.85 15.48±2.98 11.58±3.62 3.60±1.60 46.56±10.53 2.84±2.77 Welch值 37.55** 40.84** 28.38** 30.38** 58.00** 23.62** 事后检验 C3>C2>C1 C3>C2>C1 C3>C2>C1 C3>C2>C1 C3>C2>C1 C3>C2>C1 注: **P < 0.01;C1代表低发组,C2代表普通组,C3代表高发组。 -
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