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Associations Between Mental Health, Interoception, Psychological Flexibility, and Self-as-Context, as Predictors for Alexithymia: A Deep Artificial Neural Network Approach

机译:心理健康,进入,心理灵活性和自我的关联之间的关联,作为Alexithymia的预测因子:一种深入的人工神经网络方法

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Background: Alexithymia is a personality trait which is characterized by an inability to identify and describe conscious emotions of oneself and others. Aim: The present study aimed to determine whether various measures of mental health, interoception, psychological flexibility, and self-as-context, predicted through linear associations alexithymia as an outcome. This also included relevant mediators and non-linear predictors identified for particular sub-groups of participants through cluster analyses of an Artificial Neural Network (ANN) output. Methodology: Two hundred and thirty participants completed an online survey which included the following questionnaires: Toronto alexithymia scale; Acceptance and Action Questionnaire 2 (AQQII); Positive and Negative Affect Scale (PANAS-SF), Depression, Anxiety, and Stress Scale 21 (DAS21); Multidimensional Assessment of Interoceptive Awareness (MAIA); and the Self-as-Context (SAC) scale. A stepwise backwards linear regression and mediation analysis were performed, as well as a cluster analysis of the non-linear ANN upper hidden layer output. Results: Higher levels of alexithymia were associated with increased psychological inflexibility, lower positive affect scores, and lower interoception for the subscales of “not distracting” and “attention regulation.” SAC mediated the relation between emotional regulation and total alexithymia. The ANNs accounted for more of the variance than the linear regressions, and were able to identify complex and varied patterns within the participant subgroupings. Conclusion: The findings were discussed within the context of developing a SAC processed-based therapeutic model for alexithymia, where it is suggested that alexithymia is a complex and multi-faceted condition, which requires a similarly complex, and process-based approach to accurately diagnose and treat this condition.
机译:背景:Alexithymia是个性特征,其特征是无法识别和描述自己和其他人的有意识的情绪。目的:目前的研究旨在通过线性关联亚朗视为结果确定是否确定了各种心理健康,进入,心理灵活性和自我上下文的措施。这还包括通过人工神经网络(ANN)输出的集群分析来包括针对特定子组的相关介质和非线性预测器。方法论:二百三十名参与者完成了一个在线调查,其中包括以下调查问卷:多伦多Alexithymia规模;接受和行动问卷2(AQQII);正面和阴性影响量表(Panas-SF),抑郁,焦虑和压力标度21(DAS21);中断意识(MAIA)的多维评估;和自我上下文(SAC)规模。执行逐步向后的线性回归和中介分析,以及非线性ANN上隐式层输出的集群分析。结果:较高含量的亚思维症与心理屈虑令增加有关,较低的积极影响分数,以及对“不分散注意力”和“注意监管”的分量较低的间歇性。 SAC介导情绪调节与亚伦思西亚之间的关系。 ANNS占了比线性回归更多的差异,并且能够识别参与者子组中的复杂和多种模式。结论:在制定基于囊的治疗模型的亚伦西亚亚炎的治疗模型的背景下讨论了结果,其中建议亚伦西亚脲是复杂和多刻度的条件,这需要类似复杂的和基于过程的方法来准确诊断并治疗这种情况。

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