Brief Counseling for Alcohol Misuse Among Trauma Patients: Two Interventions and Influence of Baseline Use

This study examined whether baseline trauma patient characteristics and randomized participation in 1 of 2 brief interventions predicted changes in alcohol use at 6 months postintervention. Higher total Alcohol Use Disorders Identification Test (AUDIT) scores predicted greater changes, and specific AUDIT items were significant predictors.     

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.     

Supervision in Community Mental Health: Understanding Intensity of EBT Focus

The goal of the present study was to examine clinician, supervisor, and organizational factors that are associated with the intensity of evidence-based treatment (EBT) focus in workplace-based clinical supervision of a specific EBT, Trauma-Focused Cognitive Behavioral Therapy (TF-CBT). Supervisors (n = 56) and clinicians (n = 207) from mental health organizations across Washington State completed online self-report questionnaires. Multilevel modeling (MLM) analyses were used to examine the relative influence of nested clinician and supervisor factors on the intensity of EBT focus in supervision. We found that 33% of the variance in clinician report of EBT supervision intensity clustered at the supervisor level and implementation climate was the only significant factor associated with EBT supervision intensity. While individual clinician and supervisor factors may play a role in EBT coverage in supervision, our results suggest that an implementation climate that supports EBT may be the most critical factor for improving intensity of EBT coverage. Thus, implementation efforts that address the extent to which EBTs are expected, rewarded, and supported within an organization may be needed to support greater coverage of EBT during workplace-based supervision.     

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).     

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.