正念、心理灵活性和反刍在利用机器学习和结构方程建模预测大学生心理健康和幸福方面的关系

IF 4.9 Machine learning with applications Pub Date : 2025-03-01 Epub Date: 2024-12-15 DOI:10.1016/j.mlwa.2024.100614
Ruohan Feng , Vaibhav Mishra , Xin Hao , Paul Verhaeghen
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引用次数: 0

摘要

目的探讨正念、心理灵活性、反刍三者之间的复杂关系及其对心理健康和幸福感的综合影响。方法对524名大学生的调查数据进行随机森林回归,从综合的心理变量中找出显著的预测因子。然后对这些预测因子的各种组合进行神经网络训练,以评估它们在预测心理健康和幸福结果方面的表现。最后,采用结构方程模型(SEM)验证了基于确定的关键预测因子的模型,重点关注从正念到心理灵活性到反刍和幸福感的途径。结果随机森林分析表明,正念变量通过心理灵活性和反刍作用部分间接地产生影响。深层神经网络分析支持上述结果,并进一步表明正念多元模型(包括自我意识、自我调节和自我超越)在预测心理健康结果方面优于正念五面问卷变量。扫描电镜分析证实,心理灵活性,特别是其回避和接受成分,介导了正念和心理健康之间的关系。假设的一系列中介途径——正念影响心理灵活性,然后影响反刍,进而影响心理健康和福祉——得到了数据的支持。自我超越是心理健康结果的一个特别有力的预测因素。结论心理灵活性和反刍在正念对心理健康和幸福感的影响中起着重要的中介作用,表明增强正念和心理灵活性可以显著减少反刍,从而改善整体心理健康和幸福感。
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The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling

Objectives

This study explores the intricate relationships between mindfulness, psychological flexibility, rumination, and their combined impact on mental health and well-being.

Methods

Random forest regression on survey data from 524 undergraduate students was used to identify significant predictors from a comprehensive set of psychological variables. Neural networks were then trained on various combinations of these predictors to evaluate their performance in predicting mental health and well-being outcomes. Finally, structural equation modeling (SEM) was employed to validate a model based on the identified key predictors, focusing on pathways from mindfulness through psychological flexibility to rumination and well-being.

Results

The random forest analysis revealed that the mindfulness variables exerted their influence partially indirectly through psychological flexibility and rumination. The deep neural network analysis supported these findings and additionally showed that the mindfulness manifold model (consisting of self-awareness, self-regulation, and self-transcendence) was superior to the Five Facet Mindfulness Questionnaire variables in predicting mental health outcomes. The SEM analysis confirmed that psychological flexibility, particularly its avoidance and acceptance components, mediated the relationship between mindfulness and mental health. The hypothesized serial mediation pathway—mindfulness affecting psychological flexibility, which then influences rumination and subsequently mental health and well-being—was supported by the data. Self-transcendence was a particularly powerful predictor of mental health outcomes.

Conclusions

The findings underscore the critical role of psychological flexibility and rumination in mediating the effects of mindfulness on mental health and well-being, suggesting that enhancing mindfulness and psychological flexibility might significantly reduce rumination, thereby improving overall mental health and well-being.
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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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审稿时长
98 days
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