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Longitudinal network structure of depression symptoms and self-efficacy in low-income mothers

萧条(经济学) 纵向研究 医学 年轻人 精神科 老年学 心理学 经济 病理 宏观经济学
作者
Hudson P. Santos,Jolanda Kossakowski,Todd A. Schwartz,Linda S. Beeber,Eiko I. Fried
出处
期刊:PLOS ONE [Public Library of Science]
卷期号:13 (1): e0191675-e0191675 被引量:93
标识
DOI:10.1371/journal.pone.0191675
摘要

Maternal depression was recently conceptualized as a network of interacting symptoms. Prior studies have shown that low self-efficacy, as an index of maternal functioning, is one important source of stress that worsens depression. We have limited information, however, on the specific relationships between depression symptoms and self-efficacy. In this study, we used regularized partial correlation networks to explore the multivariate relationships between maternal depression symptoms and self-efficacy over time. Depressed mothers (n = 306) completed the Center for Epidemiological Studies Depression (CES-D) scale at four time points, between four and eight weeks apart. We estimated (a) the network structure of the 20 CES-D depression symptoms and self-efficacy for each time point, (b) determined the centrality or structural importance of all variables, and (c) tested whether the network structure changed over time. In the resulting networks, self-efficacy was mostly negatively connected with depression symptoms. The strongest relationships among depression symptoms were 'lonely-sleep difficulties' and 'inability to get going-crying'. 'Feeling disliked' and 'concentration difficulty' were the two most central symptoms. In comparing the network structures, we found that the network structures were moderately stable over time. This is the first study to investigate the network structure and their temporal stability of maternal depression symptoms and self-efficacy in low-income depressed mothers. We discuss how these findings might help future research to identify clinically relevant symptom-to-symptom relationships that could drive maternal depression processes, and potentially inform tailored interventions. We share data and analytical code, making our results fully reproducible.
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