An exploration of clinical teaching and learning within a preceptorship model in an acute care hospital in the Republic of Ireland

Preceptorship within clinical nurse teaching was introduced in Ireland in 2002. Little is known how this model has impacted upon the pedagogical practices of the preceptor or student learning in clinical practice. An international literature review highlighted the question of what constitutes effective teaching and learning in clinical practice which is the subject of this thesis. An exploratory qualitative design was used to examine the clinical teaching and learning within the Irish preceptorship model. The sample comprised 13 students and 13 preceptors working together on four clinical areas in one hospital. Data were collected using semi- structured interviews and documentary analysis relating to the teaching and assessment of BNSc (general) students. Main findings showed preceptors used strategies that fostered performance and understanding such as demonstration, coaching and scaffolding. Participants believed the key to effective learning was interactive dialogue and building the students' confidence within the confines of a consistent mutually respectful relationship where the preceptor had time to teach. Many variations in preceptors teaching practices were illuminated. Some preceptors' utilised teaching methods that had the potential to enhance problem solving and students' self-directed learning ability. However, many did not use or value these cognitive approaches. Yet all preceptors expected students to make appropriate judgements within the unpredictable environment of practice. The student role as learner in many preceptor- students' relationships was not well understood or valued. Some cases of good practice were elucidated where professional education was the focus of students learning. Conversely in many cases the findings suggest that the students' education was driven by service needs and values such as performance, team work and a work ethic. Other professional values such as patient empowerment and critical thinking were not a primary concern. A best practice clinical teaching and learning model is offered based on the evidence of this study; recommendations as to its further modification and development are discussed. The research demonstrated how concepts such as cognitive apprenticeship (Coli ins 2006), situated teaching and learning in communities of practice (Lave 2009, Wenger 2009), and scaffolding (Vygotsky 1978) can be helpful in understanding the processes entailed in preceptorship. Therefore the research should provide both pragmatic guidance for nurse education in Ireland and more widely, and the development of our understanding about nurse education. The latter will add to the relatively weak theoretical underpinnings of much of the existing literature in nurse education research

Preferred Qualifications: Community College Teaching Experience

Given the extremely tight job market for professional philosophers, more Ph.Ds. are beginning to consider jobs at the community college level. There are good reasons for considering this avenue: if you enjoy teaching, the job focus is on teaching, and you evaluation and tenure depend primarily on your performance in the classroom; if the prospect of working with a very diverse student body, both in terms of background and skill set, appeals to you; if the location in which you live is large part of job satisfaction, there is a far greater ability to get a job in an urban area via the community college track. However, to get a job at a community college, one thing is prized above all: teaching experience. Yet this is where the newly minted graduate student may well be at a disadvantage in the community college hiring process. In this article I seek to address the issue of how to become a strong candidate for a community college position right out of graduate school

Biography

Thesis (M.F.A.)--Boston Universit

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence

Divergence in Dialogue

Copyright: 2014 Healey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This work was supported by the Economic and Social Research Council (ESRC; http://www.esrc.ac.uk/) through the DynDial project (Dynamics of Conversational Dialogue, RES-062-23-0962) and the Engineering and Physical Sciences Research Council (EPSRC; http://www.epsrc.ac.uk/) through the RISER project (Robust Incremental Semantic Resources for Dialogue, EP/J010383/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Need for Caring

Review of AIDS AND THE LAW: A GUIDE FOR THE PUBLIC. Edited by Harlon L. Dalton, Scott Burris, and the Yale AIDS Law Project. New Haven: Yale University Press. 1987. Pp. vii, 382

Efficient tilings of de Bruijn and Kautz graphs

Kautz and de Bruijn graphs have a high degree of connectivity which makes them ideal candidates for massively parallel computer network topologies. In order to realize a practical computer architecture based on these graphs, it is useful to have a means of constructing a large-scale system from smaller, simpler modules. In this paper we consider the mathematical problem of uniformly tiling a de Bruijn or Kautz graph. This can be viewed as a generalization of the graph bisection problem. We focus on the problem of graph tilings by a set of identical subgraphs. Tiles should contain a maximal number of internal edges so as to minimize the number of edges connecting distinct tiles. We find necessary and sufficient conditions for the construction of tilings. We derive a simple lower bound on the number of edges which must leave each tile, and construct a class of tilings whose number of edges leaving each tile agrees asymptotically in form with the lower bound to within a constant factor. These tilings make possible the construction of large-scale computing systems based on de Bruijn and Kautz graph topologies.Comment: 29 pages, 11 figure