How do school counselling and therapy services undertake participation activities in a way that meets the ethical requirements of therapeutic practice?

This study explores how organisations providing counselling and therapy services within schools undertake participation activities, and the ways in which ethical considerations might have affected the choice of methods. It primarily focuses on the participation of children and young people in improving the quality of services. The research aimed to gather potential models of involving children and young people in the development of services. Two semi-structured interviews and one focus group took place, gathering data from five services providing therapeutic services in schools in London or the South East. The research found that while participative practice is inherent in the day-to-day delivery of therapy and counselling, it is still in development at an organisational level. The need for standardisation of approach at an organisational level clashed with the therapist and counsellors’ need to consider an ethical approach and the individual needs of clients. Specific challenges related to collecting the views of young children were noted in the interviews.The study concludes that devising a prescriptive approach that is delivered identically in every case is not appropriate. Every method used must be differentiated for different ages and developmental stages, and a level of flexibility in its implementation must be allowed. This flexibility will allow the therapist or counsellor to adapt the approach in situations where its delivery may conflict with ethical principles

Griffiths phase of the Kondo insulator fixed point

Heavy fermion compounds have long been identified as systems which are extremely sensitive to the presence of impurities and other imperfections. In recent years, both experimental and theoretical work has demonstrated that such disorder can lead to unusual, non-Fermi liquid behavior for most physical quantities. In this paper, we show that this anomalous sensitivity to disorder, as well as the resulting Griffiths phase behavior, directly follow from the proximity of metallic heavy fermion systems to the Kondo insulator fixed point.Comment: 5 pages, 4 figures; Proceedings of the SCES, August 2000, to appear in the Journal of Magnetism and Magnetic Material

Independent Mammalian Genome Contractions Following the KT Boundary

Although it is generally accepted that major changes in the earth's history are significant drivers of phylogenetic diversification and extinction, such episodes may also have long-lasting effects on genomic architecture. Here we show that widespread reductions in genome size have occurred in multiple lineages of mammals subsequent to the Cretaceous–Tertiary (KT) boundary, whereas there is no evidence for such changes in other vertebrate, invertebrate, or land plant lineages. Although the mechanisms remain unclear, such shifts in mammalian genome evolution may be a consequence of an increase in the efficiency of selection against excess DNA resulting from post-KT population size expansions. Independent historical changes in genome architecture in diverse lineages raise a significant challenge to the idea that genome size is finely tuned to achieve adaptive phenotypic modifications and suggest that attempts to use phylogenetic analysis to infer ancestral genome sizes may be problematical

Insights from quantum cognitive models for organizational decision making

Organizational decision making is often explored with theories from the heuristics and biases research program, which have demonstrated great value as descriptions of how people in organizations make decisions. Nevertheless, rational analysis and classical probability theory are still seen by many as the best accounts of how decisions should be made and classical probability theory is the preferred framework for cognitive modelling for many researchers. The focus of this work is quantum probability theory, an alternative probabilistic framework. Results in decision making, which appear paradoxical from a perspective of classical probability theory, may make perfect sense if one adopts quantum probability theory. We review some cognitive models of decision making based on quantum probability theory. Each of these models is based on a challenge to prescription from classical probability theory. The transition from labeling a particular behavior as irrational, by classical probability standards, to (potentially) rational (or, at any rate, not fallacious), raises interesting possibilities, including that of characterizing certain heuristics in formal, probabilistic terms

Developing Behavior Change Interventions

Peer reviewe

Changing Behavior : A Theory- and Evidence-Based Approach

Social problems in many domains, including health, education, social relationships, and the workplace, have their origins in human behavior. The documented links between behavior and social problems have sparked interest in governments and organizations to develop effective interventions to promote behavior change. The Handbook of Behavior Change provides comprehensive coverage of contemporary theory, research, and practice on behavior change. The handbook incorporates theory- and evidence-based approaches to behavior change with chapters from leading theorists, researchers, and practitioners from multiple disciplines, including psychology, sociology, behavioral science, economics, and implementation science. Chapters are organized into three parts: (1) Theory and Behavior Change; (2) Methods and Processes of Behavior Change: Intervention Development, Application, and Translation; and (3) Behavior Change Interventions: Practical Guides to Behavior Change. This chapter provides an overview of the theory- and evidence-based approaches of the handbook, introduces the content of the handbook, and provides suggestions on how the handbook may be used by different readers. The handbook aims to provide all interested in behavior change, including researchers and students, practitioners, and policy makers, with up-to-date knowledge on behavior change and guidance on how to develop effective interventions to change behavior in different populations and contexts.Peer reviewe

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research