农村青少年的心理健康与网络行为

Mental health status and internet-surfing behavior among rural adolescents

目的:研究四川农村青少年心理健康和网络行为的现况及影响因素,以及二者相互影响关系。方法:抽取2所农村中学的2745例初一和高一学生,采用中学生心理健康量表(MMHI)、自尊量表(SES)和社会支持量表(SSRS)分别评估其心理健康状况、自尊和社会支持程度,问卷调查其社会人口学特征和网络行为情况,通过非递归结构方程模型分析各因素对心理健康和网络行为的效应及二者相互关系。结果:心理健康量表平均得分为(2.1±0.7)分,其中学习压力感(2.4±0.9)分、情绪不稳定(2.4±0.8)分、焦虑(2.4±1.0)分三个维度得分较高。长时间上网行为率为32.8%(899/2745)。结构方程模型分析显示,女性、年龄越大的青少年MMHI得分越高(β=0.058、0.058,P<0.001),男性、年龄越大的青少年长时间上网行为越多(β=-0.171、0.149,P<0.001)。SES得分和SSRS得分与MMHI得分负向关联(β=-0.300、-0.263,P<0.001),并通过心理健康的中介作用间接负向影响网络行为(βi=-0.074、-0.065,P<0.010)。MMHI得分正向影响长时间上网行为(β=0.246,P<0.001);长时间上网行为也正向影响MMHI得分(β=0.008,P<0.001),但影响力较弱。结论:农村青少年总体上心理健康问题较轻,网络行为较普遍,心理健康低水平与长时间上网行为互为危险因素。

Objective： To investigate the prevalence and influencing factors of mental health status and inter- net-surfing behavior among rural adolescents in Sichuan province, and explore the mutual effects between mental health status and internet-surfing behavior. Methods： Totally 2745 junior and senior high school students of grade seven and grade ten from two rural schools were selected. Mental health status, self-esteem and social support of students were assessed with Mental Health Inventory of Middle-school students （MMHI）, Rosenberg self-esteem scale （SES） and social support rating scale （SSRS） respectively. Demographic characteristics, internet-surfing be- havior were obtained using cross-sectional survey. Non-recursive structural equation model was applied to analyze the effects of other variables on mental health status and internet-surfing behavior and the mutual effects between them. Results：The mean score of MMHI was （2. 1±0. 7）, and the dimensions including academic stress （2.4±0. 9）, emotional instability （2. 4±0. 8） and anxiety （2.4±1.0） got the top three. The total prevalence of long-time internet-surfing was 32. 8% （899/2745）. The structural equation model showed that female and increasing age had positive effects on score of MMHI （β=-0. 058, 0. 058, P 〈 0. 001）, and male and increasing age positively influenced internet-surfing behavior （β= -0. 171,0. 149, P 〈0. 001）. The scores of SES and SSRS were directly nega- tively related to the score of MMHI （β= - 0. 300, - 0. 263, P 〈 0. 001）, and indirectly negatively affect internet- surfing behavior through the mediating effect of mental health （βi= - 0. 074, - 0. 065, P 〈 0. 010）. The score of MMHI had positively effects on long-time internet-surfing behavior （β = 0. 246, P 〈 0. 001）, and long-time internet- surfing behavior had positively effects on the score of MMHI in reverse （β= 0.008, P 〈 0. 001）, but much weaker. Conclusion： There are mild mental health problem among rural adolescents, and internet-surfing behaviors are prevalent among this population. Poor mental health and long-time internet-surfing behavior are risk factors mutually.