The Convergent Validity of Four Burnout Measures in a Chinese Sample: A Confirmatory Factor‐Analytic Approach

The purpose of this paper is to study the convergent validity of the four most widely used burnout measures in a sample of Chinese nurses (N= 717). First, Structural Equation Modeling was used to investigate the factor structure of scores produced by the Maslach Burnout Inventory‐General Survey (MBI‐GS), the Burnout Measure (BM), the Shirom‐Melamed Burnout Measure (SMBM), and the Oldenburg Burnout Inventory (OLBI). Next, several competing models were tested to investigate the convergent validity of these four burnout instruments. The final results suggest that burnout is best conceived of as a multidimensional construct consisting of exhaustion and withdrawal, which are two related but conceptually distinct aspects. In addition, positively phrased items should be dropped from burnout measures for they constitute a separate factor that is considered to be an artifact.     

Perceptual learning in maze discriminations

In Experiment 1, rats were trained on a discrimination between rubber- and sandpaper-covered arms of a maze after one group had been pre-exposed to these intra-maze cues. Pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made easier by adding further discriminative stimuli, when it now significantly retarded learning. In Experiment 2, rats were trained on an extra-maze spatial discrimination, again after one group, but not another, had been pre-exposed to the extra-maze landmarks. Here too, pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made substantially easier by arranging that the two arms between which rats had to choose were always separated by 135°. The results of both experiments can be explained by supposing that perceptual learning depends on the presence of features common to S+ and S-.     

Logical Foundations for Belief Representation

This essay presents a philosophical and computational theory of the representation of de re, de dicto, nested, and quasi-indexical belief reports expressed in natural language. The propositional Semantic Network Processing System (SNePS) is used for representing and reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring in intentional contexts and representing uses of indicators by another speaker) pose problems far natural-language representation and reasoning systems, because—unlike pure indicators—they cannot be replaced by coreferential NPs without changing the meaning of the embedding sentence. Therefore, the referent of the quasi-indicator must be represented in such a way that no invalid coreferential claims are entailed. The importance of quasi-indicators is discussed, and it is shown that all four of the above categories of belief reports can be handled by a single representational technique using belief spaces containing intensional entities. Inference rules and belief-revision techniques for the system ore also examined.