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随着智能终端的普及、移动互联网和信息技术的快速发展,人们生活越来越依赖手机等信息通信技术,数字技术在人们日常生活中已成为不可或缺的部分[1]。现代信息技术的快速发展为老年人智慧养老提供了保障,探索社交媒体使用与老年人心理健康的关系,有利于智慧养老的发展。根据《2021年世界卫生统计报告》数据显示,全球约9.3%的人口年龄超过65岁,预计到2050年,这一比例将达到16%[2]。另外,第七次全国人口普查数据显示,中国65岁及以上人口比重达到13.50%,人口老龄化程度已高于世界平均水平,老龄化问题日益突出。目前,数字技术也已被广泛应用于老年人的日常生活,是老年人获取信息的重要途径[3],同时也给老年人生活带来了诸多便利。在数字化时代,以社交媒体为代表的信息通信技术对老年人的日常生活产生了极大的影响。有研究表明,社交媒体的使用可以减轻老年人孤独感,与其他年龄组的人相比老年人更容易感到孤独[4-5]。孤独是指当社会关系质量欠缺时所经历的消极情绪[6]41。有证据表明,孤独与老年人的认知能力下降、痴呆风险、早亡风险及心血管系统疾病等有关[7],另外,孤独感对生活质量、身心健康和死亡率都有较大影响[4]96。
在过去的十年里,随着社交媒体的快速发展,老年人已成为社交媒体大军中的重要一员。社交媒体的发展,为老年人孤独干预提供了新的方法[8]。研究表明,社交媒体使用影响着老年人心理健康、抑郁和孤独感等[9-12];也有研究表明,互联网使用与孤独感关系密切[13-14];此外,还有一些研究发现了社交媒体使用和孤独感之间的双向关系:一方面,孤独感会促进社交媒体使用[15-21];另一方面,社交媒体使用也会影响着孤独感的增加或减少[22-28],但是有关该方面的研究更多是集中在年轻人,而对老年人群体研究相对较少[29-31]。目前关于老年人孤独感等心理健康问题与社交媒体信息技术内在机制研究相对单一,且先前很多研究只是检验了两者间的直接关系,而对其内在机制缺乏深入研究。如在社交媒体上发布和阅读信息可以增加社会支持,从而减少孤独感[25]1626。与此同时,对不同类型的社交媒体使用进行分类,有助于解决社交媒体使用的相关问题(如社交媒体的主动和被动使用)[32-36]。进行分类后可以更加方便地捕捉和区分与社交媒体相关的不同行为的内在特征,从而探索其如何通过不同途径产生的相关影响[37]。目前,有关不同类型社交媒体使用(即主动、被动和功能型使用)是如何影响老年人孤独感的研究相对较少。
本文将老年人社交媒体使用分为主动型、被动型和功能型,对466名老年人进行了深度调研。采用PLS-SEM方法对不同类型社交媒体使用、自尊、客观社会孤立、在线社会支持、向上社会比较及社会存在与老年人孤独感进行了深入研究,揭示其内在的机制,力求为老年人孤独感的预防与干预提供研究依据与理论基础,也为未来智慧健康养老趋势下社交媒体对老年人孤独感影响等相关领域研究提供参考。
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本文对参与者采用了相同的访谈程序和量表[90],因此在PLS-SEM分析时可能存在共同方法偏差(CMB)。继而,使用多种方法来减轻和评估共同方法偏差,如通过更改不同维度的格式来减弱共同尺度属性等问题[91]539。此外,Harman的单因素检验结果表明,第一个因子解释总变异量的11.4%,这说明研究不存在严重的共同方法偏差[92]。
本文通过Cronbach’s Alpha、RHO_A和Composite Reliability(CR)值评估内部一致性,它们的标准均大于0.6[93-95]。根据Fornell和Larcker[96]提出的Average Variance Extracted(平均提取方差,以下简称AVE)来评估收敛效度,标准为0.5以上,如下所示
$$ {\rm{AVE}}=\dfrac{\left(\displaystyle\sum \lambda_{i}^{2}\right) \operatorname{var} F}{\left(\displaystyle\sum \lambda_{i}^{2}\right) \operatorname{var} F+\displaystyle\sum \Theta_{i i}} $$ (1) 其中,
$ \lambda_{i}, F $ 和$\Theta _{ii} $ 分别表示因子载荷、因子方差和误差方差。Heterotrait-monotrait Ratio(异质—单质比率,以下简称HTMT)的评价方法基于推断统计采用置信区间衡量区分效度,两维度间的HTMT不能大于0.85[97]。此外,本文删除CFA载荷较低的题项,即删除了一个主动社交媒体使用题项、一个在线社会支持题项和一个服务满意度题项。表1、表2及表3显示了修正后测量模型的信度和效度。
表 1信度和收敛效度
维度 Cronbach’s Alpha RHO_A CR AVE ASMU 0.918 0.918 0.960 0.924 FSMU 0.623 0.744 0.832 0.715 LN 0.823 0.840 0.894 0.738 LS 0.897 0.930 0.923 0.706 OSI 0.774 0.805 0.897 0.814 OSS 0.900 0.901 0.917 0.526 PSUM 0.901 0.939 0.930 0.769 SA 0.896 0.971 0.949 0.903 SEL 0.813 0.826 0.888 0.726 SP 0.803 0.814 0.871 0.629 USC 0.828 0.871 0.877 0.590 注: ASMU:主动社交媒体使用;FSMU:功能型社交媒体使用;LN:孤独感;LS:生活满意度;OSI:客观社会孤立;OSS:在线社会支持;PSMU:被动社交媒体使用;SA:服务满意度;SEL:自尊;SP:社会存在;USC:向上社会比较。 表 2区别效度
维度 ASMU FSMU LN LS OSI OSS PSUM SA SEL SP FSMU 0.677 LN 0.106 0.175 LS 0.075 0.107 0.349 OSI 0.168 0.218 0.330 0.171 OSS 0.260 0.183 0.134 0.130 0.233 PSUM 0.493 0.496 0.198 0.084 0.144 0.077 SA 0.030 0.052 0.155 0.601 0.042 0.070 0.077 SEL 0.101 0.345 0.350 0.254 0.174 0.112 0.117 0.279 SP 0.152 0.177 0.171 0.108 0.172 0.387 0.079 0.207 0.133 USC 0.063 0.113 0.170 0.173 0.229 0.198 0.285 0.121 0.311 0.239 表 3维度相关信息表(HTMT)
维度 ASMU FSMU LN LS OSI OSS PSUM SA SEL SP USC ASMU 0.961 FSMU 0.483 0.834 LN 0.090 0.096 0.859 LS 0.02 −0.043 −0.319 0.84 OSI 0.127 −0.079 −0.272 0.149 0.901 OSS 0.246 0.139 −0.057 −0.001 −0.195 0.725 PSUM −0.445 −0.416 −0.161 −0.074 −0.115 −0.01 0.877 SA 0.014 −0.038 −0.144 0.541 −0.037 0 0.062 0.950 SEL −0.085 −0.274 −0.292 0.219 0.108 0.066 0.098 0.236 0.852 SP 0.133 0.114 −0.141 0.041 −0.127 0.335 0.065 0.173 0.08 0.793 USC −0.011 0.033 0.132 −0.037 −0.173 0.131 0.266 −0.022 −0.274 0.202 0.768 注:对角线的元素是AVE的平方根。 -
通过PLS-SEM分析发现,各维度的VIF值均低于5,这表明共线性不严重。R2是一种解释力的度量,一般而言,研究模型的解释力在0.190~0.333之间表示模型可以接受的[98]。如图2所示,本文模型的决定系数R2为0.253,其中,主动、被动及功能型社交媒体使用和自尊对老年人在线社会支持的变化能够解释8.3%,主动、被动及功能型社交媒体使用、在线社会支持、向上社会比较和自尊对老年人社会存在的变化能够解释16.5%,主动、被动社交媒体使用和自尊对老年人向上社会比较的变化能够解释17.4%,功能型社交媒体使用对自尊的变化能够解释7.1%。如图3所示,通过Simple Slope分析发现[99],对于客观社会孤立程度较高的老年人,向上社会比较对老年人孤独感具有显著的正向预测作用;而对于客观社会孤立程度较低的老年人,向上社会比较对老年人孤独感具有显著的负向预测作用(客观社会孤立量表得分越高代表老人客观社会孤立程度越低)。
本文使用Efron和Tibshirani[100]、Efron[101]建议的Boostrapping重复抽样技术确定研究显著性,运行结果如图2所示,H1a、H3b、H6a、H6b、H7、H8a、H8b、H9、H10、H11、H12b在统计上得到支持。向上社会比较作为被动社交媒体使用(t=2.006,p=0.045)、自尊(t=2.029,p=0.043)和社会存在的中介变量,中介效果均显著。在线社会支持作为主动社交媒体使用和社会存在的中介变量(t=2.429,p=0.015),中介效果显著。此外,自尊作为功能型社交媒体使用和向上社会比较(t=2.285,p=0.022)及孤独感(t=1.978,p=0.048的中介变量,中介效果也均显著。结果表明,老年人的社交媒体使用与孤独感之间存在着独特的关系模式。
Research on Loneliness of the Elderly based on Smart Health Community
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摘要:智慧养老与老年人健康密切相关,现有养老模式下老年人身心健康问题较为突出。探索社交媒体使用与老年人心理健康的关系,并对其内在机制进行深入的研究。采用偏最小二乘结构方程建模(PLS-SEM)方法,以智慧健康养老社区的老年人为研究对象,探索多种社交媒体使用模式对老年人心理健康影响的内在机制(以孤独感为例),深入剖析主动社交媒体使用、被动社交媒体使用、功能型社交媒体使用、自尊、客观社会孤立、生活满意度及服务满意度与老年人孤独感之间的关系。研究结果显示:基于社会存在、社会比较与社会支持理论,不同社交媒体使用模式对老年人心理健康有显著影响;功能型社交媒体使用负向影响老年人自尊;自尊对向上社会比较和孤独感都有显著的负向预测作用。此外,客观社会孤立调节向上社交比较与孤独感之间的关系,客观社会孤立正向预测老年人孤独感。为未来采用社交媒体方式对老年人孤独感进行干预提供了研究依据,丰富了老年人心理健康相关研究,也为智慧养老发展提供了支持。Abstract:Smart elderly care is closely related to the health of the elderly, and the physical and mental health problems of the elderly under the existing pension mode are prominent. In this research, the relationship between social media use and mental health of the elderly was explored, and an in-depth study was conducted on its internal mechanism. Partial least squares structural equation modeling (PLS-SEM) method was used to explore the internal mechanism of the influence of multiple social media use patterns on the mental health of the elderly in 8 smart health elderly care communities (taking loneliness as an example). The relationship between active social media use, passive social media use, functional social media use, self-esteem, objective social isolation, life satisfaction and service satisfaction and loneliness among the elderly was analyzed. The results show that, based on social presence, social comparison and social support theory, different social media use patterns had a significant impact on the mental health of the elderly; functional social media use negatively affected the self-esteem of the elderly; self-esteem negatively predicted both upward social comparison and loneliness. In addition, objective social isolation moderated the relationship between upward social comparison and loneliness, and objective social isolation positively predicted loneliness in the elderly. This study provides a research basis for using social media to intervene loneliness in the elderly in the future, enriches the research on mental health of the elderly, and also provides support for the development of smart elderly care.
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表 1信度和收敛效度
维度 Cronbach’s Alpha RHO_A CR AVE ASMU 0.918 0.918 0.960 0.924 FSMU 0.623 0.744 0.832 0.715 LN 0.823 0.840 0.894 0.738 LS 0.897 0.930 0.923 0.706 OSI 0.774 0.805 0.897 0.814 OSS 0.900 0.901 0.917 0.526 PSUM 0.901 0.939 0.930 0.769 SA 0.896 0.971 0.949 0.903 SEL 0.813 0.826 0.888 0.726 SP 0.803 0.814 0.871 0.629 USC 0.828 0.871 0.877 0.590 注: ASMU:主动社交媒体使用;FSMU:功能型社交媒体使用;LN:孤独感;LS:生活满意度;OSI:客观社会孤立;OSS:在线社会支持;PSMU:被动社交媒体使用;SA:服务满意度;SEL:自尊;SP:社会存在;USC:向上社会比较。 表 2区别效度
维度 ASMU FSMU LN LS OSI OSS PSUM SA SEL SP FSMU 0.677 LN 0.106 0.175 LS 0.075 0.107 0.349 OSI 0.168 0.218 0.330 0.171 OSS 0.260 0.183 0.134 0.130 0.233 PSUM 0.493 0.496 0.198 0.084 0.144 0.077 SA 0.030 0.052 0.155 0.601 0.042 0.070 0.077 SEL 0.101 0.345 0.350 0.254 0.174 0.112 0.117 0.279 SP 0.152 0.177 0.171 0.108 0.172 0.387 0.079 0.207 0.133 USC 0.063 0.113 0.170 0.173 0.229 0.198 0.285 0.121 0.311 0.239 表 3维度相关信息表(HTMT)
维度 ASMU FSMU LN LS OSI OSS PSUM SA SEL SP USC ASMU 0.961 FSMU 0.483 0.834 LN 0.090 0.096 0.859 LS 0.02 −0.043 −0.319 0.84 OSI 0.127 −0.079 −0.272 0.149 0.901 OSS 0.246 0.139 −0.057 −0.001 −0.195 0.725 PSUM −0.445 −0.416 −0.161 −0.074 −0.115 −0.01 0.877 SA 0.014 −0.038 −0.144 0.541 −0.037 0 0.062 0.950 SEL −0.085 −0.274 −0.292 0.219 0.108 0.066 0.098 0.236 0.852 SP 0.133 0.114 −0.141 0.041 −0.127 0.335 0.065 0.173 0.08 0.793 USC −0.011 0.033 0.132 −0.037 −0.173 0.131 0.266 −0.022 −0.274 0.202 0.768 注:对角线的元素是AVE的平方根。 -
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