A Cloud With a Silver Lining: Helping Students Learn About Professionalism

Problem: Educators face many challenges in teaching professionalism. Despite attempts to define professionalism, it remains abstract for students and often not fully appreciated until they are in clinic. Without a way to make it personally relevant, students will likely be less motivated to learn. Intervention: We used student-generated word clouds to facilitate reflection and discussions, thereby helping students make their perceptions of professionalism more explicit. Group discussion was followed by a 5-minute written reflection. Word clouds created in Semesters 1 and 7 enabled students to compare perceptions at two points in the curriculum. Context: The George Washington University Doctor of Physical Therapy Program is a 3-year, 8-semester, 109-credit postgraduate program that includes 34 weeks of clinical practice. Reflection is foundational to the curriculum, and students reflect on their learning and professional growth each semester. Historically, students were introduced to professionalism in Semester 1 using explicit instructional strategies. Despite the wealth of resources, readings, and discussions, engagement remained challenging. Outcome: Student-generated word clouds created a personally relevant visual from which uniquely designed prompts were created to facilitate discussion. Having students compare word clouds across semesters enabled them to identify what, when, how, and where they learned about professionalism. Word clouds, categorized words, and 5-minute written reflections provided evidence of individual and collective changes in student perceptions. Lessons Learned: Students will engage in rich discussions on professionalism if it is personally relevant. Anonymity can foster discussion on personal characteristics and biases. Visualization of student-generated, narrative data enhanced reflection and discussion. Comparing word clouds from two points in time helped students articulate changes in their perceptions of professionalism. A 5-minute reflection can be a powerful learning tool for students and faculty. Outcomes demonstrated the value of designing interventions grounded in the educational principles.     

The value of simulation-based learning in pre-licensure nurse education: A state-of-the-art review and meta-analysis

Simulation modalities are numerous in nursing education, with a need to reveal their range and impact. We reviewed current evidence for effectiveness of medium to high fidelity simulation as an education mode in pre-licensure/pre-registration nurse education. A state-of-the-art review and meta-analyses was conducted based on a systematic search of publications in English between 2010 and 2015. Of 72 included studies, 43 were quantitative primary studies (mainly quasi-experimental designs), 13 were qualitative studies and 16 were reviews of literature. Forty of 43 primary studies reported benefits to student learning, and student satisfaction was high. Simulation programs provided multi-modal ways of learning. A meta-analysis (8 studies, n = 652 participants) identified that simulation programs significantly improved clinical knowledge from baseline. The weighted mean increase was 5.0 points (CI: 3.25–6.82) on a knowledge measure. Other objectively rated measures (eg, trained observers with checklists) were few. Reported subjective measures such as confidence and satisfaction when used alone have a strong potential for results bias. Studies presented valid empirical evidence, but larger studies are required. Simulation programs in pre-licensure nursing curricula demonstrate innovation and excellence. The programs should be shared across the discipline to facilitate development of multimodal learning for both pre-licensure and postgraduate nurses.     

Occlusion and brain function: mastication as a prevention of cognitive dysfunction

Summary Research in animals and humans has shown that mastication maintains cognitive function in the hippocampus, a brain area important for learning and memory. Reduced mastication, an epidemiological risk factor for the development of dementia in humans, attenuates spatial memory and causes hippocampal neurons to deteriorate morphologically and functionally, especially in aged animals. Active mastication rescues the stress‐attenuated hippocampal memory process in animals and attenuates the perception of stress in humans by suppressing endocrinological and autonomic stress responses. Active mastication further improves the performance of sustained cognitive tasks by increasing the activation of the hippocampus and the prefrontal cortex, the brain regions that are essential for cognitive processing. Abnormal mastication caused by experimental occlusal disharmony in animals produces chronic stress, which in turn suppresses spatial learning ability. The negative correlation between mastication and corticosteroids has raised the hypothesis that the suppression of the hypothalamic–pituitary–adrenal (HPA) axis by masticatory stimulation contributes, in part, to preserving cognitive functions associated with mastication. In the present review, we examine research pertaining to the mastication‐induced amelioration of deficits in cognitive function, its possible relationship with the HPA axis, and the neuronal mechanisms that may be involved in this process in the hippocampus.     

Sequential Expectations: The Role of Prediction‐Based Learning in Language

Prediction‐based processes appear to play an important role in language. Few studies, however, have sought to test the relationship within individuals between prediction learning and natural language processing. This paper builds upon existing statistical learning work using a novel paradigm for studying the on‐line learning of predictive dependencies. Within this paradigm, a new “prediction task” is introduced that provides a sensitive index of individual differences for developing probabilistic sequential expectations. Across three interrelated experiments, the prediction task and results thereof are used to bridge knowledge of the empirical relation between statistical learning and language within the context of nonadjacency processing. We first chart the trajectory for learning nonadjacencies, documenting individual differences in prediction learning. Subsequent simple recurrent network simulations then closely capture human performance patterns in the new paradigm. Finally, individual differences in prediction performances are shown to strongly correlate with participants’ sentence processing of complex, long‐distance dependencies in natural language.     

The impact of iconic gestures on foreign language word learning and its neural substrate

Vocabulary acquisition represents a major challenge in foreign language learning. Research has demonstrated that gestures accompanying speech have an impact on memory for verbal information in the speakers' mother tongue and, as recently shown, also in foreign language learning. However, the neural basis of this effect remains unclear. In a within‐subjects design, we compared learning of novel words coupled with iconic and meaningless gestures. Iconic gestures helped learners to significantly better retain the verbal material over time. After the training, participants' brain activity was registered by means of fMRI while performing a word recognition task. Brain activations to words learned with iconic and with meaningless gestures were contrasted. We found activity in the premotor cortices for words encoded with iconic gestures. In contrast, words encoded with meaningless gestures elicited a network associated with cognitive control. These findings suggest that memory performance for newly learned words is not driven by the motor component as such, but by the motor image that matches an underlying representation of the word's semantics. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.     

Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora

ObjectiveThe goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations.MethodsThe significance of this work stands on its experimental layout. The experiments were carried out under the same conditions for both languages. Several classification approaches were explored: maximum probability, CRF, Perceptron and SVM. The classifiers were enhanced by means of ensembles of semantic spaces and ensembles of Brown trees. In order to mitigate sparsity of data, without a significant increase in the dimension of the decision space, we propose the use of clustered approaches of the hierarchical Brown clustering represented by trees and vector quantization for each semantic space.ResultsThe results showed that the semi-supervised approaches significantly improved standard supervised techniques for both languages. Moreover, clustering the semantic spaces contributed to the quality of the entity recognition while keeping the dimension of the feature-space two orders of magnitude lower than when directly using the semantic spaces.ConclusionsThe contributions of this study are: (a) a set of thorough experiments that enable comparisons regarding the influence of different types of features on different classifiers, exploring two languages other than English; and (b) the use of ensembles of clusters of Brown trees and semantic spaces on EHRs to tackle the problem of scarcity of available annotated data.     

CHI: A contemporaneous health index for degenerative disease monitoring using longitudinal measurements

In this paper, we develop a novel formulation for contemporaneous patient risk monitoring by exploiting the emerging data-rich environment in many healthcare applications, where an abundance of longitudinal data that reflect the degeneration of the health condition can be continuously collected. Our objective, and the developed formulation, is fundamentally different from many existing risk score models for different healthcare applications, which mostly focus on predicting the likelihood of a certain outcome at a pre-specified time. Rather, our formulation translates multivariate longitudinal measurements into a contemporaneous health index (CHI) that captures patient condition changes over the course of progression. Another significant feature of our formulation is that, CHI can be estimated with or without label information, different from other risk score models strictly based on supervised learning. To develop this formulation, we focus on the degenerative disease conditions, for which we could utilize the monotonic progression characteristic (either towards disease or recovery) to learn CHI. Such a domain knowledge leads us to a novel learning formulation, and on top of that, we further generalize this formulation with a capacity to incorporate label information if available. We further develop algorithms to mitigate the challenges associated with the nonsmooth convex optimization problem by first identifying its dual reformulation as a constrained smooth optimization problem, and then, using the block coordinate descent algorithm to iteratively solve the optimization with a derived efficient projection at each iteration. Extensive numerical studies are performed on both synthetic datasets and real-world applications on Alzheimer’s disease and Surgical Site Infection, which demonstrate the utility and efficacy of the proposed method on degenerative conditions that include a wide range of applications.     

Optimization of infobutton design and Implementation: A systematic review

ObjectiveInfobuttons are clinical decision tools embedded in the electronic health record that attempt to link clinical data with context sensitive knowledge resources. We systematically reviewed technical approaches that contribute to improved infobutton design, implementation and functionality.MethodsWe searched databases including MEDLINE, EMBASE, and the Cochrane Library database from inception to March 1, 2016 for studies describing the use of infobuttons. We selected full review comparative studies, usability studies, and qualitative studies examining infobutton design and implementation. We abstracted usability measures such as user satisfaction, impact, and efficiency, as well as prediction accuracy of infobutton content retrieval algorithms and infobutton adoption/interoperability.ResultsWe found 82 original research studies on infobuttons. Twelve studies met criteria for detailed abstraction. These studies investigated infobutton interoperability (1 study); tools to help tailor infobutton functionality (1 study); interventions to improve user experience (7 studies); and interventions to improve content retrieval by improving prediction of relevant knowledge resources and information needs (3 studies). In-depth interviews with implementers showed the Health Level Seven (HL7) Infobutton standard to be simple and easy to implement. A usability study demonstrated the feasibility of a tool to help medical librarians tailor infobutton functionality. User experience studies showed that access to resources with which users are familiar increased user satisfaction ratings; and that links to specific subsections of drug monographs increased information seeking efficiency. However, none of the user experience improvements led to increased usage uptake. Recommender systems based on machine learning algorithms outperformed hand-crafted rules in the prediction of relevant resources and clinicians’ information needs in a laboratory setting, but no studies were found using these techniques in clinical settings. Improved content indexing in one study led to improved content retrieval across three health care organizations.ConclusionBest practice technical approaches to ensure optimal infobutton functionality, design and implementation remain understudied. The HL7 Infobutton standard has supported wide adoption of infobutton functionality among clinical information systems and knowledge resources. Limited evidence supports infobutton enhancements such as links to specific subtopics, configuration of optimal resources for specific tasks and users, and improved indexing and content coverage. Further research is needed to investigate user experience improvements to increase infobutton use and effectiveness.     

Protection of Effective Component Group from Xiaoshuan Tongluo on Brain Injury after Chronic Hypoperfusion in Rats

Objective To investigate the protective effects of purified effective component group in extract from Xiaoshuan Tongluo(CGXT) formula on chronic brain ischemia in rats.Methods CGXT 75,150,and 300 mg/kg or vehicle were ig administered daily for four weeks to rats with bilateral common carotid arteries ligation(BCCAL) .From the day 24 to 28 after BCCAL,Morris water maze was performed to assess the learning and memory impairment of rats.Four weeks after BCCAL,brain gray and white matter damage were assessed.Results In Morris test,the mean escape latency of rats in the CGXT(150 and 300 mg/kg) groups was significantly shorter than that in the vehicle group.CGXT also attenuated the neuronal damage in hippocampus and cortex and reduced the pathological damage in the optic tract and corpus callosum.Conclusion CGXT could improve learning and memory impairment resulted from BCCAL in rats.These results provide the experimental basis for the clinical use of CGXT in stroke treatment and may help in investigation of multimodal therapy strategies in ischemic cerebrovascular diseases including stroke.