Psychoanalytic thinking in primary care: The Tavistock Psychotherapy Consultation model

This paper describes the conceptual underpinnings, structure and operations of a novel service, the City and Hackney Primary Care Psychotherapy Consultation Service – a service set up partly with the aim of addressing the needs of patients who present with ‘medically unexplained symptoms’. As part of the Tavistock and Portman NHS Trust, this service moves its clinical base, staff members and daily work, as well as the foundations of psychoanalytic thinking that define the Trust's work, into the heart of a community, and provides psychoanalytically informed clinical practice and consultation to patients and general practitioners in the City and Hackney, one of London's (and the UK's) most deprived and ethnically diverse boroughs. The authors describe the psychoanalytical underpinnings of the model, the design and structure of the service, patient demographics and preliminary outcome data, as well as an example of consultation work with general practitioners. The authors propose that psychoanalytic applications have a place in primary care and that psychoanalytic thinking can help general practitioners and patients alike, even when the clinical interventions offered are not solely based on psychoanalytic technique or therapeutic approaches. The paper concludes with thoughts about the model, its origins and its future.     

The dynamics of work-family relationships in managerial lives

This article will focus on the relationship between professional and private life among successful managers, people who live outwardly meaningful work lives. We wish to report some findings from a broader study, showing the dynamic nature of this relationship which changes during the course of adult life. The latter part of the paper assesses those dynamics and their implications.     

Early Caregiver–Child Interaction and Children’s Development: Lessons from the St. Petersburg-USA Orphanage Intervention Research Project

Clinical Child and Family Psychology Review - We review a series of interrelated studies on the development of children residing in institutions (i.e., orphanages) in the Russian Federation or...     

When What You Want is What You Get: Pay Dispersion and Communal Sharing Preference

The question of whether pay structures should be compressed or dispersed remains unanswered. We argue that pay dispersion can yield uncertainty regarding others' intentions and behaviors; as a result, individuals take a greater risk trusting their group members as pay spreads widen. Accordingly, we explore the conditions under which individuals are more willing to take this risk by viewing their group members as trustworthy even when pay is dispersed. Specifically, preferences for how relationships and resources should be structured in groups should help to determine when pay dispersion relates to trustworthiness perceptions. We hypothesise that the cross‐level interaction between preferences for communal sharing (Level 1)—that is, the extent to which individuals prefer communal, egalitarian structures in their groups—and pay dispersion (Level 2) is associated with trust perceptions. Data drawn from a sample of university professors support our hypothesised cross‐level interaction, and show that when pay dispersion is greater, individuals perceive their group members as more trustworthy only when they have weak preferences for communal sharing. Our results signify the importance of individual attributes to understanding pay dispersion's effects, and show that trust is fostered when preferences and pay conditions are aligned.     

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data

Abstract

Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this article, a latent block distance-association model is proposed to apply block clustering to the outcomes of two categorical variables while the cluster centers are represented in a low dimensional space in terms of a distance-association model. This model is particularly useful for contingency tables in which both the rows and the columns are characterized as profiles of sets of response variables. The parameters are estimated under a Poisson sampling scheme using a generalized EM algorithm. The performance of the model is tested in a Monte Carlo experiment, and an empirical data set is analyzed to illustrate the model.