Weak ventral striatal responses to monetary outcomes predict an unwillingness to resist cigarette smoking

As a group, cigarette smokers exhibit blunted subjective, behavioral, and neurobiological responses to nondrug incentives and rewards, relative to nonsmokers. Findings from recent studies suggest, however, that there are large individual differences in the devaluation of nondrug rewards among smokers. Moreover, this variability appears to have significant clinical implications, since reduced sensitivity to nondrug rewards is associated with poorer smoking cessation outcomes. Currently, little is known about the neurobiological mechanisms that underlie these individual differences in the responsiveness to nondrug rewards. Here, we tested the hypothesis that individual variability in reward devaluation among smokers is linked to the functioning of the striatum. Specifically, functional magnetic resonance imaging was used to examine variability in the neural response to monetary outcomes in nicotine-deprived smokers anticipating an opportunity to smoke—circumstances found to heighten the devaluation of nondrug rewards by smokers in prior work. We also investigated whether individual differences in reward-related brain activity in those expecting to have access to cigarettes were associated with the degree to which the same individuals subsequently were willing to resist smoking in order to earn additional money. Our key finding was that deprived smokers who exhibited the weakest response to rewards (i.e., monetary gains) in the ventral striatum were least willing to refrain from smoking for monetary reinforcement. These results provide evidence that outcome-related signals in the ventral striatum serve as a marker for clinically meaningful individual differences in reward-motivated behavior among nicotine-deprived smokers.     

Research-Based Knowledge in Psychology: What,if Anything,is Its Incremental Value to the Practitioner?

This essay reflects an ongoing dialogue between a clinician versed in mainstream psychological research and theory, and a social psychologist with experience both as a researcher and contributor to applied undertakings in various domains about the “incremental value” of research-based knowledge—that is, its value beyond that provided by the other sources of knowledge available to the practitioner. These sources include knowledge about the needs and coping strategies of all human beings, as well as knowledge both about the specific life circumstances of those one is seeking to help, and knowledge about language and culture. Examples from the clinical practice of the first author are offered, coupled with in-principle arguments about the underspecified and contingent nature of research-based generalizations. By way of rebuttal, examples of arguably useful findings are provided by the second author—especially findings that serve as correctives to biases in lay psychology (notably unwarranted “dispositionism”) and to widespread shortcomings in judgment and decision-making (particularly, Kahneman and Tversky’s work on “prospect theory” ). Both authors agree on the value of a “bricoleur” treatment strategy that relies on careful attention to the specifics of the case at hand and avoids one-size-fits-all applications of theory and prior research, and both agree that research-based findings are more useful in predicting behavior and designing intervention strategies that apply to groups and large samples of individuals rather than single actors. A concluding discussion focuses on necessary criteria and strategies for increasing the usefulness of laboratory and field research for the practitioner.     

Change in Maternal Stress for Families in Treatment for their Children with Oppositional Defiant Disorder

Our objective was to predict change in maternal stress over the course of a randomized clinical trial comparing the efficacy of two interventions for Oppositional Defiant Disorder (ODD): Parent Management Training and Collaborative & Proactive Solutions. In a secondary analysis of data collected from this randomized clinical trial, we examined whether children’s self-reported positive relations with their parents impacted responsiveness to treatment, which in turn impacted maternal stress. One hundred thirty-four children and their parents (38.1% female, ages 7–14, M age?=?9.51, SD?=?1.77) were tracked across three time points: pre-treatment; one-week post-treatment; and six-month post-treatment. Hierarchical linear models tested change in children’s reports of positive relations with parents, clinician reports of ODD severity, and maternal reports of parenting stress. Models then tested multilevel mediation from positive relations with parents, through ODD severity, onto maternal stress. Hypothesized indirect effects were supported such that children’s reports of positive views toward parents uniquely predicted reductions in ODD severity over time, which in turn uniquely predicted reductions in maternal stress. Results highlight the promise of potential secondary benefits for parents following interventions for children with oppositional problems. Furthermore, results underscore the importance of the parent–child relationship as both a protective factor and as an additional target to complement interventions for child disruptive behaviors.     

Redefining unforgiveness: Exploring victims’ experiences in the wake of unforgiven interpersonal transgressions

This study explored victims’ experiences in the aftermath of unforgiven offenses. Semi-structured interviews with 13 individuals discussing two unforgiven offenses revealed considerable variability in the experiences of unforgiveness, suggesting that unforgiveness may be more multifaceted than previously believed. In addition to the negative emotions and rumination previously posited to characterize unforgiveness, novel themes emerged describing unforgiving cognitions (e.g., the offense is unforgiveable) and construals of the offender. Participants varied along each dimension, resulting in a range of outcomes (e.g., forgiveness, lingering personal distress, finding peace without forgiving). Implications for conceptualizing unforgiveness and counseling those who struggle with unforgiven offenses are discussed.     

A Review of Best Practice Recommendations for Text Analysis in R (and a User-Friendly App)

In recent decades, the amount of text available for organizational science research has grown tremendously. Despite the availability of text and advances in text analysis methods, many of these techniques remain largely segmented by discipline. Moreover, there is an increasing number of open-source tools (R, Python) for text analysis, yet these tools are not easily taken advantage of by social science researchers who likely have limited programming knowledge and exposure to computational methods. In this article, we compare quantitative and qualitative text analysis methods used across social sciences. We describe basic terminology and the overlooked, but critically important, steps in pre-processing raw text (e.g., selection of stop words; stemming). Next, we provide an exploratory analysis of open-ended responses from a prototypical survey dataset using topic modeling with R. We provide a list of best practice recommendations for text analysis focused on (1) hypothesis and question formation, (2) design and data collection, (3) data pre-processing, and (4) topic modeling. We also discuss the creation of scale scores for more traditional correlation and regression analyses. All the data are available in an online repository for the interested reader to practice with, along with a reference list for additional reading, an R markdown file, and an open source interactive topic model tool (topicApp; see https://github.com/wesslen/topicApp, https://github.com/wesslen/text-analysis-org-science, https://dataverse.unc.edu/dataset.xhtml?persistentId=doi:10.15139/S3/R4W7ZS).     

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.     

Letting Go: Conceptualizing Intervention De‐implementation in Public Health and Social Service Settings

The discontinuation of interventions that should be stopped, or de‐implementation, has emerged as a novel line of inquiry within dissemination and implementation science. As this area grows in human services research, like public health and social work, theory is needed to help guide scientific endeavors. Given the infancy of de‐implementation, this conceptual narrative provides a definition and criteria for determining if an intervention should be de‐implemented. We identify three criteria for identifying interventions appropriate for de‐implementation: (a) interventions that are not effective or harmful, (b) interventions that are not the most effective or efficient to provide, and (c) interventions that are no longer necessary. Detailed, well‐documented examples illustrate each of the criteria. We describe de‐implementation frameworks, but also demonstrate how other existing implementation frameworks might be applied to de‐implementation research as a supplement. Finally, we conclude with a discussion of de‐implementation in the context of other stages of implementation, like sustainability and adoption; next steps for de‐implementation research, especially identifying interventions appropriate for de‐implementation in a systematic manner; and highlight special ethical considerations to advance the field of de‐implementation research.