Residual Effects of Slavery: A Delphi Study

Slavery and its aftermath have created multiple challenges that are unique to the African American community. Although literature exists within other disciplines, no published empirical study within the marriage and family therapy literature has examined the residual effects of slavery. We used a modified Delphi methodology to explore the residual effects of slavery on African Americans and to determine resultant clinical implications. To obtain information from panelists who have expertise in this area, we conducted three rounds of data collection: an open-ended questionnaire, a Likert-scale questionnaire, and a round of in-depth interviews. Based on our analysis of the data collected, this empirical study describes the influence of the residual effects of slavery, implications for clinicians and directions for future research.

     

Latinx Parents’ Perception of Discrimination and Ethnic/Racial Socialization Predict Their Elementary School Children’s Perceptions of Discrimination

Journal of Child and Family Studies - The present study examined whether Latinx parents’ perceptions of personal discrimination and perception of biases against Latinx people in the United...     

The relationship between multilevel models and non‐parametric multilevel mixture models: Discrete approximation of intraclass correlation,random coefficient distributions,and residual heteroscedasticity

Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non‐parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non‐parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non‐standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed.     

“Transformation Tuesday”: Temporal context and post valence influence the provision of social support on social media

Social network sites (SNSs) such as Facebook have become integral in the development and maintenance of interpersonal relationships. Users of SNSs seek social support and validation, often using posts that illustrate how they have changed over time. The purpose of the present research is to examine how the valence and temporal context of an SNS post affect the likelihood of other users providing social support. Participants viewed hypothetical SNS posts and reported their intentions to provide social support to the users. Results revealed that participants were more likely to provide social support for posts that were positive and included temporal context (i.e., depicted improvement over time; Study 1). Furthermore, this research suggests that visual representations of change over time are needed to elicit social support (Study 2). Results are discussed in terms of their practical implications for SNS users and theoretical implications for the literature on social support and social media.     

Perspectives on Increasing Positive Attitudes Toward Larger Members in Fitness Centers

Physical activity (PA) attenuates weight-associated morbidity and mortality but PA-based weight bias dissuades many people from engaging in and enjoying physical activity. Guided by self-determination theory, we implemented concept mapping with current and past members and employees of fitness centers. One hundred ideas were generated, representing the concepts of programming, fitness culture, code of conduct, professional development, and physical environment/amenities. The ideas can help design interventions that increase positive attitudes that are autonomous, shared throughout a fitness center, and promote inclusivity.     

Delivering Acceptance and Commitment Therapy for Weight Self-Stigma Through Guided Self-Help: Results From an Open Pilot Trial

Weight self-stigma is a promising target for innovative interventions seeking to improve outcomes among overweight/obese individuals. Preliminary research suggests acceptance and commitment therapy (ACT) may be an effective approach for reducing weight self-stigma, but a guided self-help version of this intervention may improve broad dissemination. This pilot open trial sought to evaluate the potential acceptability and efficacy of a guided self-help ACT intervention, included coaching and a self-help book, with a sample of 13 overweight/obese individuals high in weight self-stigma. Results indicated a high degree of program engagement (77% completed the intervention) and satisfaction. Participants improved on outcomes over time including weight self-stigma, emotional eating, weight management behaviors, health-related quality of life, and depression. Although not a directly targeted outcome, participants improved on objectively measured weight, with an average of 4.18 pounds lost over 7 weeks, but did not improve on self-reported weight at 3-month follow-up. Processes of change improved over time, including psychological inflexibility, valued action and reasons to lose weight. Coaching effects indicated greater retention and improvements over time with one coach vs. the other, suggesting characteristics of coaching can affect outcomes. Overall, these results provide preliminary support for the acceptability and efficacy of a guided self-help ACT program for weight self-stigma. Implications of these results and how to address clinical challenges with guided self-help are discussed.     

Activating Veterans Toward Sources of Reward: A Pilot Report on Development,Feasibility, and Clinical Outcomes of a 12-Week Behavioral Activation Group Treatment

This pilot study evaluates a 12-week group Behavioral Activation protocol adapted to meet the needs of a Veteran population seeking treatment in an outpatient mental health clinic at a Veteran Affairs Medical Center. In a detailed Method we describe the treatment structure. Acceptability and feasibility are addressed by providing data on referral sources, treatment retention, attendance, and patient satisfaction. Initial clinical outcomes are presented, focusing on symptom reduction, improved quality of life, and changes in the hypothesized mechanism of treatment: improving motivated behavior to pursue rewards (decisional anhedonia). Finally, feedback from individual exit interviews is presented. We conclude with implementation tips and challenges in the service of continuing to improve our evidence-based interventions in Veteran Affairs facilities.     

The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity

Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as “black teenagers” are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.