Medical diagnostic reasoning: Epistemological modeling as a strategy for design of computer-based consultation programs

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient's findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient's characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems.     

Relative deprivation,national GDP and right-wing populism: A multilevel,multinational study

We conducted a secondary analysis of the 2018 (ninth round) European Social Survey dataset (N = 19,512, nested in 24 countries) to analyse the association between relative deprivation and right-wing populist voting, moderated by participants' income and their country's GDP. A multilevel moderated model showed that relative deprivation, income and GDP had no association with right-wing populist votes by themselves. However, income and GDP moderated the relation between relative deprivation and right-wing populist votes, that was significant for participants with high incomes and for those living in wealthy countries. The strengths, limitations and future developments of the study are discussed.     

Innovation across cultures: Connecting leadership,identification, and creative behavior in organizations

Innovation is considered essential for today's organizations to survive and thrive. Researchers have also stressed the importance of leadership as a driver of followers' innovative work behavior (FIB). Yet, despite a large amount of research, three areas remain understudied: (a) The relative importance of different forms of leadership for FIB; (b) the mechanisms through which leadership impacts FIB; and (c) the degree to which relationships between leadership and FIB are generalizable across cultures. To address these lacunae, we propose an integrated model connecting four types of positive leadership behaviors, two types of identification (as mediating variables), and FIB. We tested our model in a global data set comprising responses of N = 7,225 participants from 23 countries, grouped into nine cultural clusters. Our results indicate that perceived LMX quality was the strongest relative predictor of FIB. Furthermore, the relationships between both perceived LMX quality and identity leadership with FIB were mediated by social identification. The indirect effect of LMX on FIB via social identification was stable across clusters, whereas the indirect effects of the other forms of leadership on FIB via social identification were stronger in countries high versus low on collectivism. Power distance did not influence the relations.