心理健康
概念化
心理学
老年学
意义(存在)
相关性
住所
荟萃分析
医学
临床心理学
人口学
精神科
社会学
几何学
数学
人工智能
计算机科学
内科学
心理治疗师
作者
Shu Nie,Jiyoon Lim,Xintian Xu,Lei Zheng,Yiqun Gan
出处
期刊:The international journal of mental health promotion
[Computers, Materials and Continua (Tech Science Press)]
日期:2023-01-01
卷期号:25 (9): 971-984
标识
DOI:10.32604/ijmhp.2023.029155
摘要
This study aims to be the first to use meta-analysis to explore the relationship between meaning in life (MIL) and mental health issues among older adults. A meta-analysis was conducted using six databases, resulting in 16 studies with 5,074 participants in total. The “metacor” and “forestplot” packages in R-Studio were used for data analysis. The total effect was calculated using a random-effects model, with I² = 86% in the heterogeneity test. The results showed a moderate negative correlation between MIL and mental health issues among older adults, with an average effect of −0.37. Five potential moderating variables were examined: the conceptualization of MIL (value vs. purpose), region (Asian vs. Western countries), residence status (community vs. nursing home vs. hospital), types of mental health issues, and evaluation methods (clinical vs. non-clinical). The first four had no significant moderating effect. The mean correlation coefficients between mental health issues and value/purpose were −0.49/−0.33; the mean correlation coefficients in Asian countries and Western countries were −0.48 and −0.34; the mean correlation coefficients among participants living in community/nursing home/mixed status were −0.33/−0.40/−0.40; the mean correlation coefficients between MIL and depression/others were −0.37/−0.35; however, the negative relationship between MIL and mental health issues was stronger when non-clinical evaluations (self-report only) were used. Specifically, the mean correlation coefficient for non-clinical evaluations was −0.42 and for clinical evaluations was −0.29. This study is the first meta-analysis to identify the negative correlation between older adults’ MIL and mental health issues. Significant moderating effects of evaluation methods were found.
科研通智能强力驱动
Strongly Powered by AbleSci AI