Definition: To what extent does the study allow us to draw conclusions about a causal effect between two or more constructs? Issues: Selection, maturation, history, mortality, testing, regression towrd the mean, selection by maturation, treatment by mortality, treatment by testing, measured treatment variables Increase: Eliminate the threats, above all do experimental manipulations, random assignment, and counterbalancing.

Reliability and validity

Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on post-traumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks.

/pdf/a-tutorial-on-regularized-partial-correlation-networks-4vtbhqb1xk.pdf

A Tutorial on Regularized Partial Correlation Networks

Borderline Conditions and Pathological Narcissism

Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

/pdf/a-tutorial-on-regularized-partial-correlation-networks-c1sjtfgq58.pdf

A tutorial on regularized partial correlation networks.

In this chapter, we will outline how the concept of mentalizing helps us understand some of the common problems associated with borderline personality disorder and how the theoretical understanding is translated into psychotherapeutic intervention. Mentalizing is the process by which we interpret the actions of ourselves and others in terms of underlying intentional states such as personal desires, needs, feelings, beliefs and reasons. This capacity develops within the context of attachment relationships during infancy and childhood. Borderline personality disorder is conceived of as being a disorder of mentalizing. Vulnerability to a loss in mentalizing particularly in interpersonal or stressful circumstances is a core feature of the disorder. If treatment is to be successful it must either have mentalization as its focus or at the very least stimulate development of mentalizing as an epiphenomenon. Treatment focusing on mentalizing itself is described. © Copyright (c) by The Haworth Press, Inc. All rights reserved.

Mentalization-based treatment for BPD

We aim to provide quantitative evidence on the psychological impact of epidemic/pandemic outbreaks (i.e., SARS, MERS, COVID-19, ebola, and influenza A) on healthcare workers (HCWs). Forty-four studies are included in this review. Between 11 and 73.4% of HCWs, mainly including physicians, nurses, and auxiliary staff, reported post-traumatic stress symptoms during outbreaks, with symptoms lasting after 1–3 years in 10–40%. Depressive symptoms are reported in 27.5–50.7%, insomnia symptoms in 34–36.1%, and severe anxiety symptoms in 45%. General psychiatric symptoms during outbreaks have a range comprised between 17.3 and 75.3%; high levels of stress related to working are reported in 18.1 to 80.1%. Several individual and work-related features can be considered risk or protective factors, such as personality characteristics, the level of exposure to affected patients, and organizational support. Empirical evidence underlines the need to address the detrimental effects of epidemic/pandemic outbreaks on HCWs’ mental health. Recommendations should include the assessment and promotion of coping strategies and resilience, special attention to frontline HCWs, provision of adequate protective supplies, and organization of online support services.

/pdf/the-psychological-impact-of-epidemic-and-pandemic-outbreaks-3jcd0kp9du.pdf

The Psychological Impact of Epidemic and Pandemic Outbreaks on Healthcare Workers: Rapid Review of the Evidence.

Psychometric properties of the Italian version of the SCL-90-R: A study on a large community sample

Stability and variability of personality networks. A tutorial on recent developments in network psychometrics

SCL-90-R Symptom Checklist-90-RAdattamento italiano

Increasing evidence suggests that individuals with borderline personality disorder (BPD) might feel rejected even when socially included by others. A psychological mechanism accounting for this response bias could be that objective social inclusion violates BPD patients' underlying implicit needs of "extreme" inclusion. Thus, this study investigated whether, during interpersonal exchanges, BPD patients report more rejection-related negative emotions and less feelings of social connection than controls unless they are faced with conditions of extreme social inclusion. Sixty-one BPD patients and 61 healthy controls completed a modified Cyberball paradigm. They were randomly assigned to a condition of ostracism, social inclusion, or overinclusion (a proxy for extreme social inclusion). They then rated their emotional states and feelings of social connection immediately and 20 min after the game. BPD patients reported greater levels of negative emotions than controls in the ostracism and the inclusion conditions, but not when overincluded. Furthermore, only for BPD participants was overinclusion associated with experiencing less negative emotions than the ostracism condition. However, BPD patients reported lower feelings of social connection than controls in any experimental situation. Thus, in BPD, a laboratory condition of "overinclusion" is associated with a reduction of negative emotions to levels comparable to those of control participants, but not with similar degrees of social connection. These results suggest that for BPD patients, even "including contexts" activate feelings of rejection. Their implicit expectations of idealized interpersonal inclusion may nullify the opportunity of experiencing "real" social connection and explain their distorted subjective experiences of rejection.

/pdf/when-social-inclusion-is-not-enough-implicit-expectations-of-5g1894z8qa.pdf

Emanuele Preti

Papers

The Psychological Impact of Epidemic and Pandemic Outbreaks on Healthcare Workers: Rapid Review of the Evidence.

Psychometric properties of the Italian version of the SCL-90-R: A study on a large community sample

Stability and variability of personality networks. A tutorial on recent developments in network psychometrics

SCL-90-R Symptom Checklist-90-RAdattamento italiano

When social inclusion is not enough: Implicit expectations of extreme inclusion in borderline personality disorder.