Profiles of Mindfulness across Adulthood
Profiles of Mindfulness across Adulthood
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DOI：
10.1007/s12671-020-01372-z
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发表时间：
2020-04-15
期刊：
影响因子：
3.6
通讯作者：
Shook, Natalie J.
中科院分区：
文献类型：
--
作者：
Ford, Cameron G.;Wilson, Jenna M.;Shook, Natalie J.
以人为中心的分析方法（例如，潜在轮廓分析，聚类分析）已被提供作为与五面正念问卷（FFMQ）相关的测量问题的潜在解决方案。然而，现有的文献利用以人为中心的方法揭示了在确定的正念概况中缺乏一致性，特别是在非大学样本中。本研究采用潜在特征分析和聚类分析方法，在成人生命周期和社区样本中检验了FFMQ特征的普遍性。此外，该研究还探讨了正念概况是否与年龄和幸福感有关。通过亚马逊的土耳其机器人招募的不同年龄的参与者（N = 715）完成了FFMQ和许多幸福感测量。结果聚类分析显示4种正念特征：(1)高正念、(2)低正念、(3)判断性观察和(4)非判断性意识。潜在剖面分析显示了四个剖面，但只有两个剖面与聚类分析得出的剖面相似，其中两个剖面占样本总数的比例不到9%。通过聚类分析发现，年龄越大，进入高正念状态的可能性越大，进入低正念状态的可能性越低。此外，高正念形象表现出最好的幸福感，低正念形象表现出最差的幸福感。总的来说，这些发现表明，分析方法的类型和样本特征，如年龄，可能会影响所得正念剖面的构成。对这一文献状态的含义进行了讨论。
Objectives Person-centered analytic approaches (e.g., latent profile analysis, cluster analysis) have been offered as a potential solution to measurement issues associated with the Five Facet Mindfulness Questionnaire (FFMQ). Yet, extant literature utilizing person-centered approaches reveals a lack of consistency in the identified mindfulness profiles, especially in non-college samples. The present study tested the generalizability of FFMQ profiles in an adult life span, community sample using latent profile analysis and cluster analysis. Furthermore, the study explored whether mindfulness profiles related to age and well-being. Methods Age-diverse participants (N = 715) recruited through Amazon's Mechanical Turk completed the FFMQ and numerous measures of well-being. Results Cluster analysis revealed four mindfulness profiles: (1) high mindfulness, (2) low mindfulness, (3) judgmentally observing, and (4) nonjudgmentally aware. Latent profile analysis indicated four profiles, but only two profiles resembled profiles resulting from the cluster analysis, and two of the profiles comprised less than 9% of the sample combined. Using profiles identified by cluster analysis, older age was associated with increased likelihood of classification into a high mindfulness profile and decreased likelihood of classification into a low mindfulness profile. Furthermore, the high mindfulness profile showed the best well-being and the low mindfulness profile showed the worst. Conclusions Overall, these findings demonstrate that the type of analytic method and sample characteristics, such as age, may affect the makeup of resulting mindfulness profiles. Implications for the state of this literature are discussed.