Producing the magnum opus : A metaphor for nephrology nursing expertise acquisition

Aim. This paper elucidates the nature of metaphor and the conditions necessary to its use as an analytic device in qualitative research, and describes how the use of metaphor assisted in the analytic processes of a grounded theory study of nephrology nursing expertise. Background. The use of metaphor is pervasive in everyday thought, language and action. It is an important means for the comprehension and management of everyday life, and makes challenging or problematic concepts easier to explain. Metaphors are also pervasive in quantitative and qualitative research for the same reason. In both everyday life and in research, their use may be implicit or explicit. Methods. The study using grounded theory methodology took place in one renal unit in New South Wales, Australia between 1999 and 2000 and included six non-expert and 11 expert nurses. It involved simultaneous data collection and analysis using participant observation, semi-structured interviews and review of nursing documentation. Findings. A three stage skills-acquisitive process was identified in which an orchestral metaphor was used to explain the relationships between stages and to satisfactorily capture the data coded within each stage. Conclusion. Metaphors create images, clarify and add depth to meanings and, if used appropriately and explicitly in qualitative research, can capture data at highly conceptual levels. Metaphors also assist in explaining the relationship between findings in a clear and coherent manner. © 2005 Blackwell Publishing Ltd.

The acquisition and exercise of nephrology nursing expertise : A grounded theory study

Aims and objectives. This purpose of this study was to describe the process of expertise acquisition in nephrology nursing practice. Background. It has been recognized for a number of decades that experts, compared with other practitioners in a number of professions and occupations, are the most knowledgeable and effective, in terms of both the quantity and quality of output. Studies relating to expertise have been undertaken in a range of nursing contexts and specialties; to date, however, none have been undertaken which focus on nephrology nursing. Design. This study, using grounded theory methodology, took place in one renal unit in New South Wales, Australia and involved six non-expert and 11 expert nurses. Methods. Simultaneous data collection and analysis took place using participant observation, semi-structured interviews and review of nursing documentation. Findings. The study revealed a three-stage skills-acquisitive process that was identified as non-expert, experienced non-expert and expert stages. Each stage was typified by four characteristics, which altered during the acquisitive process; these were knowledge, experience, skill and focus. Conclusion. This was the first study to explore nephrology nursing expertise and uncovered new aspects of expertise not documented in the literature and it also made explicit other areas, which had only been previously implied. Relevance to clinical practice. Of significance to nursing, the exercise of expertise is a function of the recognition of expertise by others and it includes the blurring of the normal boundaries of professional practice. © 2006 Blackwell Publishing Ltd.

Online discovery of similarity mappings

We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.