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Unraveling the particular Hand in hand Catalytic Connection between TiO2 and also Pr6O11 upon Exceptional Dehydrogenation Shows regarding α-AlH3.
From a clinical perspective, parental emotion regulation is of utmost importance due to the degree of parental involvement in interventions for childhood emotional and behavioral disorders, which are often aimed at promoting child self-regulation. To illustrate practical applications of Eisenberg's model, we discuss evidence-based practices that include enhancement of parent emotion regulation as a primary target, with the ultimate goal of promoting child emotional development. Ultimately, we aim to spur future theoretical, empirical, and translational work in this area. (PsycINFO Database Record (c) 2020 APA, all rights reserved).We examined the extent to which new mothers' recollections of their mothers' emotion socialization practices during childhood predict sensitive/supportive responses to their own toddlers in distressing situations both directly and indirectly via effects on mothers' social information processing about infant cry signals. Mothers' adult attachment was tested as a possible moderator and we tested model invariance across racial groups. These questions were assessed using a 3-wave longitudinal study of first-time mothers (131 African American, 128 European American) followed from pregnancy until children were 14 months old. Expectant mothers were administered the Adult Attachment Interview and self-report measures of remembered childhood emotion socialization. When infants were 6 months old, mothers' cry processing was assessed using a video-recall method in which they watched videos of their interactions with their infants during distress tasks and reported on their emotions and cognitions during the interaction. Maternal sensitivity to distress at 14 months was assessed via observed maternal sensitivity during distress tasks and mothers' self-reported responses to child distress. Consistent with prediction, mothers who recalled their own mothers as high on nonsupportive responses to their distress in childhood engaged in more self-focused and negative cry processing at 6 months, which in turn predicted less supportive responding to their toddlers in distressing situations. This indirect effect was statistically significant. These effects were not moderated by adult attachment coherence. The full model was invariant across racial groups. Thus, remembered childhood emotion socialization experiences have longstanding consequences for subsequent social behavior, including parenting the next generation. (PsycINFO Database Record (c) 2020 APA, all rights reserved).Over 20 years ago, Eisenberg, Cumberland, and Spinrad (1998; Eisenberg, Spinrad, & Cumberland, 1998) published a landmark article focusing on the socialization of children's emotion and self-regulation, including emotion regulation. In this special issue, our goal was to compile current evidence delineating the impact of emotion-related socialization behaviors (ERSBs) on children's emotion, self-regulation, and developmental outcomes. NVS-STG2 in vivo The work in this issue highlights the processes involved in predicting both parents' ERSBs as well as children's developmental outcomes. Researchers have moved beyond testing individual "pieces" of the socialization of emotion model and now use innovative and sophisticated methods for testing larger models, allowing for more causal interpretations. Special issue contributors focused on longitudinal studies including ERSBs, reviews of the literature extending the original model, and the effectiveness of interventions designed to improve the emotional lives of children and their families. We focus on some of the major themes of the special issue and conclude with recommendations for policies and programs to promote youths' effective emotion-related outcomes. (PsycINFO Database Record (c) 2020 APA, all rights reserved).In this qualitative study, we explored the experiences of clients receiving cognitive behavioral therapy (CBT) for major depressive disorder. All participants received 8 sessions of traditional CBT (based on Beck, Rush, Shaw, & Emergy, 1979) and 8 sessions of positive CBT (order counterbalanced). The aim of the study was to examine clients' experience of positive CBT and to contrast this with their experience of traditional CBT. Positive CBT structurally and selectively focuses on better moments (exceptions to the problem as opposed to the problem), strengths, and positive emotions and integrates traditional CBT with solution-focused brief therapy and positive psychology. In addition to conducting interviews with 12 individuals, the second author attended all therapy sessions of 4 clients and observed biweekly supervision sessions as further methods of data collection. Qualitative analysis showed that, despite initial skepticism, clients preferred positive CBT and indicated experiencing a steeper learning curve during positive, compared with traditional, CBT for depression. The popularity of positive CBT was attributable to 4 influences feeling good and empowered, benefitting from upward spiral effects of positive emotions, learning to appreciate baby steps, and (re)discovering optimism as a personal strength. Qualitative analysis showed that, despite better moments and building positivity efficiently counters depressive symptoms and builds well-being. Clients perceived positive CBT's upbeat tone as stimulating and as motivating for change. (PsycINFO Database Record (c) 2020 APA, all rights reserved).Gaussian graphical models are commonly used to characterize conditional (in)dependence structures (i.e., partial correlation networks) of psychological constructs. Recently attention has shifted from estimating single networks to those from various subpopulations. The focus is primarily to detect differences or demonstrate replicability. We introduce two novel Bayesian methods for comparing networks that explicitly address these aims. The first is based on the posterior predictive distribution, with a symmetric version of Kullback-Leibler divergence as the discrepancy measure, that tests differences between two (or more) multivariate normal distributions. The second approach makes use of Bayesian model comparison, with the Bayes factor, and allows for gaining evidence for invariant network structures. This overcomes limitations of current approaches in the literature that use classical hypothesis testing, where it is only possible to determine whether groups are significantly different from each other. With simulation we show the posterior predictive method is approximately calibrated under the null hypothesis (α = .
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