Distress after a single violent crime: how shame-proneness and event-related shame work together as risk factors for post-victimization symptoms

To increase understanding of post-victimization symptom development, the present study investigated the role of shame- and guilt-proneness and event-related shame and guilt as potential risk factors. 35 individuals (M age = 31.7 yr.; 48.5% women), recently victimized by a single event of severe violent crime, were assessed regarding shame- and guilt-proneness, event-related shame and guilt, and post-victimization symptoms. The mediating role of event-related shame was investigated with structural equation modeling (SEM), using bootstrapping. The guilt measures were unrelated to each other and to post-victimization symptoms. The shame measures were highly intercorrelated and were both positively correlated to more severe post-victimization symptom levels. Event-related shame as mediator between shame-proneness and post-victimization symptoms was demonstrated by prevalent significant indirect effects. Both shame measures are potent risk factors for distress after victimization, whereby part of the effect of shame-proneness on post-victimization symptoms is explained by event-related shame.     

Assessment of self-conscious emotions: a Swedish psychometric and structure evaluation of the Test of Self-Conscious Affect (TOSCA)

The Test of Self-Conscious Affect (TOSCA) is a well-established scenario-based questionnaire assessing self-conscious emotions, such as shame and guilt, which have been shown to be differentially associated with a variety of functional, motivational, behavioral and health outcomes. The aim of this study was to evaluate the psychometric properties and internal structure of a Swedish version of TOSCA in a sample of 361 healthy adults. The psychometric properties and internal consistency of the Swedish version were at level with the original US TOSCA version for shame, guilt and detachment. The internal structure of the Swedish version was acceptable for shame, guilt and detachment but contained shortcomings in assessment of externalization.     

Towards an interreligious understanding of Hikma

In contemporary Islamic thought the dichotomy betweenrevelation andreason has emerged as a crucial issue, reinforced by cultural conflicts between East and West. Thus S. H. Nasr puts divinely inspired knowledge —sapientia orhikma — at the heart of Muslim culture, claiming God‐less rationality,scientia, to be characteristic of the Occident. This analogy is used as an established fact in some writings of apologetic nature. At the same time, the traditional concept of ‘wisdom’,hikma, is brought up to date in order to serve a new purpose in a world of increasingly specialized sciences. Whereas many Muslim writers recognize the need for an informed approach to ‘all branches of knowledge’, most of them insist on retaining a link between science and ethical values.Hikma is now launched as the authentic Islamic answer to ‘the confusion created by profane philosophies’. As the Islamic way of making science,hikma is seen as holistic and God‐centred in contrast to the Western type of science. Not all Muslim intellectuals, however, are satisfied with one single concept for the entirety of Islamic thought. Hasan Hanafi highlightshikma and Shari ^c a as ‘twin sisters, nursed at the same bosom'; Nasr proposes a hierarchy of knowledge with ‘Divine wisdom’ at the top. Related ideas can be found among some Christian theologians of religion, who have suggested that people of living faiths try to rediscover their shared heritage in ‘the universal economy ofhokhmah’, which may come to serve as a useful interreligious concept.     

PLS model building: A multivariate approach to personality test data

The aim of this study was to demonstrate how personality test data can be plotted with a multivariate method known as Partial Least Squares of Latent Structures (PLS). The basic methodology behind PLS modeling is presented and the example demonstrates how a PLS model of personality test data can be used for diagnostic prediction. Principles for validating the models are also presented. The conclusion is that PLS modeling appears to be a powerful method for extracting clinically relevant information from complex personality test data matrixes. It could be used as a complement to more hard modeling methods in the process of examining a new area of interest.