Analysis of Multiple Partially Ordered Responses to Belief Items with Don’t Know Option

Understanding beliefs, values, and preferences of patients is a tenet of contemporary health sciences. This application was motivated by the analysis of multiple partially ordered set (poset) responses from an inventory on layman beliefs about diabetes. The partially ordered set arises because of two features in the data—first, the response options contain a Don’t Know (DK) option, and second, there were two consecutive occasions of measurement. As predicted by the common sense model of illness, beliefs about diabetes were not necessarily stable across the two measurement occasions. Instead of analyzing the two occasions separately, we studied the joint responses across the occasions as a poset response. Few analytic methods exist for data structures other than ordered or nominal categories. Poset responses are routinely collapsed and then analyzed as either rank ordered or nominal data, leading to the loss of nuanced information that might be present within poset categories. In this paper we developed a general class of item response models for analyzing the poset data collected from the Common Sense Model of Diabetes Inventory. The inferential object of interest is the latent trait that indicates congruence of belief with the biomedical model. To apply an item response model to the poset diabetes inventory, we proved that a simple coding algorithm circumvents the requirement of writing new codes such that standard IRT software could be directly used for the purpose of item estimation and individual scoring. Simulation experiments were used to examine parameter recovery for the proposed poset model.     

Short theta burst stimulation to left frontal cortex prior to encoding enhances subsequent recognition memory

Deep semantic encoding of verbal stimuli can aid in later successful retrieval of those stimuli from long-term episodic memory. Evidence from numerous neuropsychological and neuroimaging experiments demonstrate regions in left prefrontal cortex, including left dorsolateral prefrontal cortex (DLPFC), are important for processes related to encoding. Here, we investigated the relationship between left DLPFC activity during encoding and successful subsequent memory with transcranial magnetic stimulation (TMS). In a pair of experiments using a 2-session within-subjects design, we stimulated either left DLPFC or a control region (Vertex) with a single 2-s train of short theta burst stimulation (sTBS) during a semantic encoding task and then gave participants a recognition memory test. We found that subsequent memory was enhanced on the day left DLPFC was stimulated, relative to the day Vertex was stimulated, and that DLPFC stimulation also increased participants’ confidence in their decisions during the recognition task. We also explored the time course of how long the effects of sTBS persisted. Our data suggest 2 s of sTBS to left DLPFC is capable of enhancing subsequent memory for items encoded up to 15 s following stimulation. Collectively, these data demonstrate sTBS is capable of enhancing long-term memory and provide evidence that TBS protocols are a potentially powerful tool for modulating cognitive function.     

Developing Social Entrepreneurship as an Intervention to Enhance Disadvantaged Young People’s Sense of Self-Worth and Career Competence in Hong Kong

Applied Research in Quality of Life - This is the first empirical research in Hong Kong to examine the impact of social entrepreneurship training on disadvantaged young peoples’ career...