清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Temporal associations among loneliness, anxiety, and depression during the COVID‐19 pandemic period

孤独 焦虑 心理学 萧条(经济学) 临床心理学 2019年冠状病毒病(COVID-19) 精神科 医学 疾病 内科学 宏观经济学 经济 传染病(医学专业)
作者
Jianfen Wu,Yunpeng Wu,Yu Tian
出处
期刊:Stress and Health [Wiley]
卷期号:38 (1): 90-101 被引量:27
标识
DOI:10.1002/smi.3076
摘要

Numerous studies have reported that individuals' loneliness, anxiety, and depression levels increased during the COVID-19 pandemic period. However, reciprocal associations among loneliness, anxiety, and depression, as well as gender differences in these associations, have not been investigated. Therefore, temporal associations among loneliness, anxiety, and depression and gender differences in these associations were examined in a longitudinal study during the COVID-19 pandemic period. The loneliness, anxiety, and depression levels of 458 university students were evaluated at three timepoints (T1, T2, and T3) during the COVID-19 pandemic period in China. The timepoints were separated by 1 month. Cross-lagged panel designs were used to examine reciprocal associations among loneliness, anxiety, and depression as well as the stability and gender differences of these associations. Cross-lagged panel analysis revealed that T1 depression positively predicted T2 anxiety and loneliness, T1 loneliness positively predicted T2 depression, T2 anxiety positively predicted T3 depression, T2 depression positively predicted T3 anxiety and loneliness, T2 loneliness positively predicted T3 depression, and T1 loneliness positively predicted T3 anxiety through the mediating role of T2 depression. No gender differences were observed in the cross-lagged associations. During the COVID-19 pandemic period, loneliness and depression predicted each other across time, and loneliness predicted anxiety across time, mediated by depression. No gender differences were observed in the cross-lagged associations among loneliness, anxiety, and depression.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
han完成签到 ,获得积分10
1秒前
智者雨人完成签到 ,获得积分10
28秒前
感动初蓝完成签到 ,获得积分10
44秒前
changfox完成签到,获得积分10
49秒前
喜悦向日葵完成签到 ,获得积分10
1分钟前
Elytra完成签到,获得积分10
1分钟前
1分钟前
Charles发布了新的文献求助10
1分钟前
1分钟前
耍酷的书本完成签到 ,获得积分10
1分钟前
忘忧Aquarius完成签到,获得积分0
1分钟前
姜丝罐罐n完成签到 ,获得积分10
1分钟前
神勇的天问完成签到 ,获得积分10
1分钟前
so0123完成签到,获得积分10
2分钟前
2分钟前
2分钟前
ccc发布了新的文献求助10
2分钟前
佳言2009完成签到 ,获得积分10
3分钟前
Lion完成签到,获得积分10
3分钟前
foyefeng完成签到,获得积分0
3分钟前
lovelife完成签到,获得积分10
3分钟前
Lucas应助科研通管家采纳,获得10
3分钟前
菜鸟学习完成签到 ,获得积分0
3分钟前
知性的雅彤完成签到,获得积分10
4分钟前
Hello应助江郁清采纳,获得10
4分钟前
艳艳宝完成签到 ,获得积分10
5分钟前
田様应助ccc采纳,获得10
5分钟前
zxx完成签到 ,获得积分10
5分钟前
yummy弯完成签到 ,获得积分10
5分钟前
恒牙完成签到 ,获得积分0
5分钟前
大力的银耳汤完成签到,获得积分10
6分钟前
1437594843完成签到 ,获得积分10
6分钟前
ccc完成签到,获得积分10
6分钟前
缓慢怜菡举报水土洼求助涉嫌违规
6分钟前
笨笨完成签到 ,获得积分10
6分钟前
LeoBigman完成签到 ,获得积分10
6分钟前
jlwang完成签到,获得积分10
7分钟前
koto留下了新的社区评论
7分钟前
DiJia完成签到 ,获得积分10
7分钟前
池东漾完成签到 ,获得积分10
8分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6353116
求助须知:如何正确求助?哪些是违规求助? 8167966
关于积分的说明 17191352
捐赠科研通 5409118
什么是DOI,文献DOI怎么找? 2863594
邀请新用户注册赠送积分活动 1840960
关于科研通互助平台的介绍 1689819