The impact of a simulated intervention on attitudes of undergraduate nursing and medical students towards end of life care provision

Background: The concerns of undergraduate nursing and medical students’ regarding end of life care are well documented. Many report feelings of emotional distress, anxiety and a lack of preparation to provide care to patients at end of life and their families. Evidence suggests that increased exposure to patients who are dying and their families can improve attitudes toward end of life care. In the absence of such clinical exposure, simulation provides experiential learning with outcomes comparable to that of clinical practice. The aim of this study was therefore to assess the impact of a simulated intervention on the attitudes of undergraduate nursing and medical students towards end of life care.
Methods: A pilot quasi-experimental, pretest-posttest design. Attitudes towards end of life care were measured using the Frommelt Attitudes Towards Care of the Dying Part B Scale which was administered pre and post a simulated clinical scenario. 19 undergraduate nursing and medical students were recruited from one large Higher Education Institution in the United Kingdom.
Results: The results of this pilot study confirm that a simulated end of life care intervention has a positive impact on the attitudes of undergraduate nursing and medical students towards end of life care (p < 0.001).
Conclusions: Active, experiential learning in the form of simulation teaching helps improve attitudes of undergraduate nursing and medical students towards end of life. In the absence of clinical exposure, simulation is a viable alternative to help prepare students for their professional role regarding end of life care.

Learning to read with a critical eye: cultivating discerning readers of media reports with a science component.

It is important for young people to be able to read science-related media reports with discernment. ‘Getting Newswise’ was a research project designed to enable science and English teachers, working collaboratively, to equip pupils through the curriculum with critical reading skills appropriate for science news. Phase one of the study found that science and English teachers respond differently to science news articles and eight categories of critical response were identified. These findings informed phase two, in which classroom activities were devised whereby pupils examined, evaluated and responded to science-related news reports. Science-English collaboration had positive outcomes for pupil understanding

Enlivening, Invigorating & Enhancing Science:This session explores learning from a range of Primary Science Teaching Trust projects focused on invigorating the experience of children, teachers and whole school communities with primary science.

This session will provide you with opportunity to find out what is being achieved and explore the implications for your own practice.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

Virtual Reality as a tool to Assist Learning in Aerospace Assembly

This study examines whether virtual reality (VR) is more superior to paper-based instructions in increasing the speed at which individuals learn a new assembly task. Specifically, the work seeks to quantify any learning benefits when individuals have been given the opportunity and compares the performance of two groups using virtual and hardcopy media types to pre-learn the task. A build experiment based on multiple builds of an aircraft panel showed that a group of people who pre-learned the assembly task using a VR environment completed their builds faster (average build time 29.5% lower). The VR group also made fewer references to instructional materials (average number of references 38% lower) and made fewer errors than a group using more traditional, hard copy instructions. These outcomes were more pronounced during build one with differences in build time and number of references showing limited statistical differences.

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.