Search results

To examine how clinicians navigate providing treatment to Borderline Personality Disorder (BPD) in the context of the DSM 5, deinstitutionalization, and the biomedical model.

We conducted 39 interviews with mental health providers in the United States in a two-year period preceding and following the release of the DSM 5. Using Constructivist Grounded Theory, we analyzed the data for themes that emerged.

Clinicians faced pressures from insurance companies, the DSM categories, and their professional training to focus on biomedical treatments. These treatments, which emphasized pharmaceuticals and short courses of care, were ill-suited to BPD, which has a strong evidence base recommending long-term therapeutic interventions. We term this contradiction a “biomedical mismatch” and use Gidden’s concept of structuration to better understand how clinicians navigate the system of care. Providers ranged in their responses to the mismatch: some championed biomedicine, others were complicit, and a final group behaved as activists, challenging the paradigm. The sum of the strategies had downstream effects which included crisis reinstitutionalization and a discourse of untreatability. Ultimately, we discuss how social factors such as gender bias, stigma, and trauma are insufficiently represented in the biomedical model of care for BPD.

BPD fits poorly within the biomedical underpinnings of the current system. Accordingly, it illuminates the structuration of health care and where the rules of care break down. More precisely, deinstitutionalization was designed to remove patients from long courses of inpatient care. Many patients with BPD have failed to experience this outcome, with some patients now cycling through long courses of short-term crisis reinstitutionalization instead of having effective outpatient care over long periods. This unintended consequence of deinstitutionalization calls for a more biopsychosocial response to BPD.

Scholars and practitioners in the OB literature nowadays appreciate that emotions and emotional regulation constitute an inseparable part of work life, but the HRM literature has lagged in addressing the emotional dimensions of life at work. In this chapter therefore, beginning with a multi-level perspective taken from the OB literature, we introduce the roles played by emotions and emotional regulation in the workplace and discuss their implications for HRM. We do so by considering five levels of analysis: (1) within-person temporal variations, (2) between persons (individual differences), (3) interpersonal processes; (4) groups and teams, and (5) the organization as a whole. We focus especially on processes of emotional regulation in both self and others, including discussion of emotional labor and emotional intelligence. In the opening sections of the chapter, we discuss the nature of emotions and emotional regulation from an OB perspective by introducing the five-level model, and explaining in particular how emotions and emotional regulation play a role at each of the levels. We then apply these ideas to four major domains of concern to HR managers: (1) recruitment, selection, and socialization; (2) performance management; (3) training and development; and (4) compensation and benefits. In concluding, we stress the interconnectedness of emotions and emotional regulation across the five levels of the model, arguing that emotions and emotional regulation at each level can influence effects at other levels, ultimately culminating in the organization’s affective climate.

Research in industrial and organizational psychology demonstrates that the regulation of negative emotions in response to both organizational stressors and interpersonal workplace interactions can result in functional and dysfunctional outcomes (Côté, 2005; Diefendorff, Richard, & Yang, 2008). Research on the regulation of negative emotions has additionally been conducted in social psychology, developmental psychology, neuropsychology, health psychology, and clinical psychology. A close reading of this broader literature, however, reveals that the conceptualization and use of the term “emotion regulation” varies within each research field as well as across these fields. The main focus of our chapter is to make sense of the term “emotion regulation” in the workplace by considering its use across a broad range of psychology disciplines. We then develop an overarching theoretical framework using disambiguating terminology to highlight what we argue are the important constructs involved in the process of intrapersonal emotion generation, emotional experience regulation, and emotional expression regulation in the workplace (e.g., emotional intelligence, emotion regulation strategies, emotion expression displays). We anticipate this chapter will enable researchers and industrial and organizational psychologists to identify the conditions under which functional regulation outcomes are more likely to occur and then build interventions around these findings.

Purpose – The purpose of this chapter is to introduce readers to augmented reality (AR), mixed reality (MR), and virtual reality (VR) and provide examples of some of the latest ways that researchers and practitioners are applying these digital technologies to emotions-related topics. This chapter also suggests some aspects of these technologies that emotions researchers and practitioners consider taking advantage of in their own work.

Design/Methodology/Approach – The chapter draws on the first author's experience developing and implementing AR, MR, and VR for serious games applications. Examples are also drawn from recent publications in the area.

Findings – The chapter discusses the features and differences between AR, MR, and VR and some of the most popular off-the-shelf tools for researchers and practitioners. It also presents reliable and valid ways these digital technologies have been applied and can be applied.

Practical implications – Practically, this chapter provides a state-of-the-art overview of what AR, MR, and VR offer to researchers and practitioners interested in better understanding, supporting, and addressing phenomenon involving human emotion.

This is the second in a series of papers focused on alcohol and substance abuse rehabilitation centers. Centers face the ongoing challenge of validating outcomes to meet the burden of evidence for insurance companies. In the first paper, data mining was used to establish baseline patterns in treatment success rates, for the Futures: Palm Beach Rehabilitation Center, that have a direct impact on a client’s ability to receive insurance coverage for treatment programs. In this paper, we examine 2016 outcomes and report on facility efficacy, alumni progression and sobriety, and forecast treatment success rates (short and long term) in support of client insurability. Data collection has been standardized and includes admissions data, electronic medical records data, satisfaction survey data, post-discharge survey data, Centers for Disease Control (CDC) data, and demographic data. Clustering, partitioning, ANOVA, stepwise regression and stepwise Logistic regression are applied to the data to determine statistically significant drivers of treatment success.