New Research From Clinical Psychological Science

Cognitive Inhibition in Trauma Recovery Among Asylum Seekers: Test in a Randomized Trial of Mindfulness-Based Trauma Recovery for Refugees
Iftach Amir, Anna Aizik-Reebs, Kim Yuval, Yuval Hadash, and Amit Bernstein 

Mindfulness-based interventions may improve the mental health and trauma recovery of forcibly displaced people. Amir and colleagues tested traumatized East African asylum seekers living in a high-risk urban postdisplacement setting. They measured participants’ cognitive inhibition (CI) of trauma- and threat-related information before and after either the intervention (Mindfulness-Based Trauma Recovery for Refugees [MBTR-R]) or a parallel waitlist-control period. At preintervention, the researchers found an association between participants’ deficit in the CI of trauma- and threat-related information and the severity of posttraumatic stress disorder (PTSD). Although MBTR-R led to improved CI of trauma- and threat-related information, this change did not mediate MBTR-R’s therapeutic effect on PTSD.   

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts
Michael E. Aristodemou et al.

A multicausal approach might be better than relying solely on one theory to understand psychopathology. Aristodemou and colleagues compared two competing frameworks used to explain the development of psychopathology: the dynamic-mutualism theory and the common-cause theory. They formalized these theories in statistical models and applied them to explain data regarding change in the general factor of psychopathology (p factor) from early to late adolescence and major depression in middle adulthood and old age. Mutualism was better than common cause at explaining change in the p factor, though not all predictions made by mutualism were supported. However, neither model fully explained changes in depression.   

Stress Accumulation, Depressive Symptoms, and Sleep Problems Among Black Americans in the Rural South
Olutosin Adesogan, Justin A. Lavner, Sierra E. Carter, and Steven R. H. Beach  

Multiple stressors stemming from systemic racism can undermine health among Black Americans, this research suggests. Adesogan and colleagues analyzed data from 692 Black adults in the rural South to examine the ways in which neighborhood stress, financial strain, and interpersonal experiences of racial discrimination operate independently and in tandem to affect depressive symptoms and sleep problems over time. Results indicated that stress has unique and additive effects on depressive symptoms and sleep problems. These findings highlight the need for further research on factors that promote well-being in the face of stressors associated with systemic racism.   

Parent and Child Depressive Symptoms and Authoritarian Parenting: Reciprocal Relations From Early Childhood Through Adolescence
Emma Chad-Friedman et al.  

Chad-Friedman and colleagues examined reciprocal relations between parent and child depressive symptoms and authoritarian-parenting behaviors across development. Mothers and fathers completed self-report measures about their depressive symptoms and authoritarian-parenting behaviors during the years their children were 3 to 15 years old. The researchers assessed child depressive symptoms using clinical interviews. Results indicated reciprocal pathways between maternal (but not paternal) and child depressive symptoms from ages 3 to 15 years. Although child depressive symptoms at age 3 years led to greater maternal and paternal negative authoritarian parenting from ages 3 to 15 years, the reverse was not observed.   

Multiple Adaptive Attention-Bias-Modification Programs to Alter Normative Increase in the Error-Related Negativity in Adolescents
Nader Amir et al.  

Attention training may modify anxiety and its biomarkers, this research suggests. Amir and colleagues examined the impact of two home-delivered attentional-bias-modification (ABM) programs on a biomarker of anxiety (i.e., the error-related negativity [ERN], a negative deflection in the event-related potential that can be recorded at the scalp via electroencephalogram). They measured the ERN, self- and parent-reported anxiety, attention bias, and attention control before and after two versions of ABM training and a waitlist control group in youths. The ABM version designed to increase attention control changed the ERN but had no impact on reported anxiety, whereas the version designed to reduce attentional bias changed attentional bias and youth’s self-reported anxiety but had no impact on the ERN or parent-reported anxiety.   

Identifying Treatment Responders to Varenicline for Alcohol Use Disorder Using Two Machine-Learning Approaches
Erica N. Grodin et al.  

Varenicline is a medication that has shown promise for treating alcohol use disorder (AUD), but not everyone responds to it. To identify treatment responders, Grodin and colleagues used machine-learning methods to examine data from the National Institute on Alcohol Abuse and Alcoholism Clinical Intervention Group clinical trial of varenicline. Results indicated that smoking status, AUD severity, medication adherence, and treatment drinking goal (e.g., total abstinence, occasional drinking) predicted treatment response, as did the interaction between age and cardiovascular health. The medication had stronger effects among individuals with lower alcohol craving. These findings illustrate how the use of machine-learning methods may inform clinical practice. 

“One Metric to Rule Them All”: A Common Metric for Symptoms of Depression and Generalized Anxiety in Adolescent Samples
Matthew Sunderland et al.  

Sunderland and colleagues identified a coherent common metric that facilitates the comparison of scores from six different but related scales of depression and generalized anxiety. Using an adolescent sample, they employed a nonequivalent-anchor-test design in conjunction with simultaneous calibration. The common metric obtained was closely aligned with the “distress” subfactor of the Hierarchical Taxonomy of Psychopathology model, and its precision level was acceptable across the more severe and often clinical levels. However, its precision at lower, “nonclinical” levels may be insufficient for detailed assessment. The authors caution that additional validation testing in independent samples is required.   

Machine-Learning-Based Prediction of Client Distress From Session Recordings
Patty B. Kuo et al.  

Natural language processing (NLP) is a subfield of machine learning that can provide feedback to therapists on client outcomes and improve client-outcome prediction, this research suggests. Kuo and colleagues developed NLP models to predict client-symptom improvement, taking into account contextual linguistic complexities. They used 2,630 session recordings from 795 clients and 56 therapists to develop NLP models that accurately predicted client symptoms on a given session based on their previous session. These results highlight the potential for the implementation of NLP models to improve the quality of care.   

Do Symptom Severity, Individual Socioeconomic Status, and Neighborhood Socioeconomic Status Explain Differences in Daily Functioning in Non-Latinx Black, Non-Latinx White, and Latinx People With Serious Mental Illnesses?
Arundati Nagendra et al.  

Nagendra and colleagues examined differences in daily functioning in White, Black, and Latinx participants diagnosed with serious mental illnesses. Participants completed ecological momentary assessments of what they were doing, who they were with, and where they were three times daily for 30 days. Black participants more frequently reported being alone or engaged in passive leisure (e.g., watching TV) than White and Latinx participants, less frequently reported vocational activity (i.e., related to an occupation or training) than Latinx participants, and less frequently reported home-based active leisure (e.g., playing a musical instrument) than White participants. Although neighborhood socioeconomic status (SES), individual SES, and symptom severity accounted for some of the findings, there is also a need to explore sociocultural and racism-related explanatory factors, Nagendra and colleagues said.   

Recommendations for Adjudicating Among Alternative Structural Models of Psychopathology
Irwin D. Waldman et al. 

Waldman and colleagues make recommendations for comparing and choosing models of psychopathology. Using simulations and analyses of the literature and contrasting the models’ reliability, the researchers highlight the shortcomings of conventional model-fit indices and propose alternative criteria for evaluating and contrasting competing models. These criteria include model characteristics (e.g., the magnitude and consistency of factor loadings and their precision), the consistency and sensitivity of factors to their constituent indicators, and the variance the model explains. Using these criteria in addition to conventional fit indices might facilitate adjudication among alternative structural models of psychopathology, Waldman and colleagues write. 

A Mixed-Methods Study of Race-Based Stress and Trauma Affecting Asian Americans During COVID
Joyce P. Yang, Quyen A. Do, Emily R. Nhan, and Jessica A. Chen  

COVID-19 propelled anti-Asian racism around the world. Yang and colleagues examined the experiences of racism during COVID and resulting psychological sequelae in Asian participants of 15 ethnicities. They found emotional, cognitive, and behavioral changes resulting from racialized perpetrations, including internalizing emotions of fear, sadness, and shame; negative alterations in cognitions, such as reduced trust and self-worth; and behavioral isolation, avoidance, and hypervigilance, in addition to positive coping actions of commitment to racial equity initiatives. They also found that individuals who experienced COVID discrimination showed higher racial trauma and posttraumatic stress disorder scores compared with individuals who did not.   

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