A Network Approach to the Five-Facet Model of Mindfulness: Insights from Gaussian Graphical and Directed Acyclic Graph Models

Despite the large-scale dissemination of mindfulness-based interventions, debates persist about the very nature of mindfulness. To date, one of the dominant views is the five-facet approach, which suggests that mindfulness includes five facets (i.e., Observing, Describing, Nonjudging, Nonreactivity, and Acting with Awareness). However, uncertainty remains regarding the potential interplay between these facets. In this study, we investigated the five-facet model via network analysis in an unselected sample (n = 1,704). We used two distinct computational network approaches: a Gaussian graphical model (i.e., undirected) and a directed acyclic graph, with each model determining the relations between the facets and their relative importance in the network. Both computational approaches pointed to the facet denoting Acting with Awareness as playing an especially potent role in the network system. Altogether, our findings offer novel data-driven clues for the field's larger quest to ascertain the very foundations of mindfulness.

尽管基于正念的干预措施已大规模传播，但对于正念本质的争论依然存在。迄今为止，五种特征方法占据主导地位，该方法认为正念包括五个方面（即观察、描述、非评判、非反应性和有意识行动）。然而，这些方面之间可能存在的相互作用仍然存在不确定性。在本研究中，我们通过网络分析对未选择样本（n = 1,704）中的五种特征模型进行了探讨。我们采用了两种不同的计算网络方法：高斯图模型（即无向图）和有向无环图，每个模型都确定了方面之间的关系及其在网络中的相对重要性。两种计算方法均指向“有意识行动”这一方面在网络系统中发挥着尤为显著的作用。总体而言，我们的发现为该领域对正念本质更深层次的探寻提供了新颖的数据驱动线索。