The Associations between Depressive Symptoms and Self-Rated Health in Relation to Sense of Coherence among Adolescents: Cross-Sectional Study

临床心理学 心理干预 抑郁症状 心理学 感觉 横断面研究 多级模型 自评健康 医学 精神科 老年学 社会心理学 计算机科学 机器学习 病理 焦虑
作者
Vilija Malinauskienė,Romualdas Malinauskas
出处
期刊:Children (Basel) [Multidisciplinary Digital Publishing Institute]
卷期号:11 (10): 1244-1244
标识
DOI:10.3390/children11101244
摘要

Background: We investigated the predictors of poor SRH in a representative sample of Lithuanian mainstream school students in grades 7–8. We also checked for gender differences in the associations between SRH and depressive symptoms and other predictors. Methods: A total of 2104 7th–8th-grade students participated (response rate 73.95%) and were asked about depressive symptoms, psychosomatic health complaints, negative acts at school, feeling at school, family stress and violence, sense of coherence, self-esteem, and lifestyle. We used a hierarchical regression analysis including a variety of self-rated health predictors. Results: Boys scored significantly higher on physical activity and smoking, whereas girls scored significantly higher on SRH, depressive symptoms, psychosomatic health complaints, and family stress and violence, though the significance was lost in the hierarchical regression. Depressive symptoms were the strongest predictor of poor SRH (standardized β = 0.309, p < 0.001), though other investigated predictors were also significant but had lower effect sizes. Strong evidence was found supporting the buffering role of sense of coherence in the relationship between depressive symptoms and SRH (standardized β = −0.266, p < 0.001). Conclusions: We can conclude that the magnitude of the relationship between depressive symptoms and self-rated health is dependent on the levels of sense of coherence. We did not find gender differences in those associations. As poor SRH is easy to determine, especially with a one-item question, the cases of poorly rated health should be detected early and corrected by interventions in order to prevent poor health outcomes in the future.

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
老实易蓉应助一陈天下采纳,获得10
1秒前
1秒前
痞子毛应助闻塔采纳,获得10
1秒前
miamikk发布了新的文献求助10
1秒前
Lizhenhua完成签到,获得积分10
2秒前
传奇3应助开放素采纳,获得10
3秒前
3秒前
活力太阳完成签到,获得积分10
4秒前
question500完成签到,获得积分10
4秒前
沈随便发布了新的文献求助10
5秒前
科研通AI6.2应助Seven采纳,获得20
7秒前
小方发布了新的文献求助10
7秒前
ding应助吉吉采纳,获得10
7秒前
8秒前
W坏蛋happy发布了新的文献求助10
9秒前
辣子鸡完成签到,获得积分10
9秒前
10秒前
李健应助爱笑的曼寒采纳,获得10
12秒前
12秒前
科研通AI6.4应助乐乐采纳,获得10
13秒前
nav发布了新的文献求助10
14秒前
lanjinglin完成签到,获得积分10
14秒前
大模型应助LSY采纳,获得10
14秒前
可爱的函函应助沈随便采纳,获得10
17秒前
在水一方应助吉吉采纳,获得10
17秒前
17秒前
18秒前
完美世界应助W坏蛋happy采纳,获得10
20秒前
小七完成签到,获得积分10
20秒前
蔡能涛完成签到 ,获得积分10
20秒前
20秒前
20秒前
Songyuxuan完成签到,获得积分10
20秒前
斯人完成签到 ,获得积分10
21秒前
poieu发布了新的文献求助30
21秒前
JunpengGuo发布了新的文献求助10
22秒前
小韩发布了新的文献求助10
24秒前
小方发布了新的文献求助10
24秒前
25秒前
26秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
Matrix Methods in Data Mining and Pattern Recognition 510
Reading and Understanding Health Research 500
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7251359
求助须知:如何正确求助?哪些是违规求助? 8873897
关于积分的说明 18729930
捐赠科研通 6931105
什么是DOI,文献DOI怎么找? 3199375
关于科研通互助平台的介绍 2374325
邀请新用户注册赠送积分活动 2173997