Focusing on patients : differences between the practice of expert and non-expert nephrology nurses

Specialisation in nursing enables a nurse to focus, in much greater depth, on the requisite knowledge and skills for providing patients with the best possible care. Nephrology nursing is one such area where specialisation has evolved. The characteristic focus of practice emerged as an important feature during a study into the process of expertise acquisition in nephrology nursing practice. Using grounded theory methodology, this study involved 6 non-expert and 11 expert nurses and took place in one renal unit in New South Wales. Nephrology nursing practice was observed for 103 hours, and this was immediately followed by semi-structured interviews. The characteristic of focus was conceptualised as the nurses' centre of attention or concentration while they were undertaking nursing activities. Focus ranged from inexperienced non-expert nurses concentrating predominantly on the immediate task at hand, experienced non-expert nurses who focussed on the medium term to expert nurses who viewed actions (and their possible consequences) more broadly and in the longer term. Of significance to nursing, is how nephrology nurses alter their focus of practice as they acquire and exercise their developing expertise in this specialty.

Understanding the role of knowledge in the practice of expert nephrology nurses in Australia

This paper, which is abstracted from a larger study into the acquisition and exercise of nephrology nursing expertise, aims to explore the role of knowledge in expert practice. Using grounded theory methodology, the study involved 17 registered nurses who were practicing in a metropolitan renal unit in New South Wales, Australia. Concurrent data collection and analysis was undertaken, incorporating participants' observations and interviews. Having extensive nephrology nursing knowledge was a striking characteristic of a nursing expert. Expert nurses clearly relied on and utilized extensive nephrology nursing knowledge to practice. Of importance for nursing, the results of this study indicate that domain-specific knowledge is a crucial feature of expert practice.

Uncovering the evidence of non-expert nephrology nursing practice

Expertise in nursing has been widely studied although there have been no previous studies into what constitutes expertise in nephrology (renal) nursing. This paper, which is abstracted from a larger study into the acquisition and exercise of nephrology nursing expertise, provides evidence of the characteristics and practices of non-expert nephrology nurses. Using the grounded theory method, the study took place in one renal unit in New South Wales, Australia, and involved six non-expert and 11 expert nurses. Sampling was purposive then theoretical. Simultaneous data collection and analysis using participant observation, review of nursing documentation and semistructured interviews was undertaken. The study revealed a three-stage skills-acquisitive process that was identified as non-expert, experienced non-expert and expert stages. Non-expert nurses showed superficial nephrology nursing knowledge and limited experience; they were acquiring basic nephrology nursing skills and possessed a narrow focus of practice.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the “ideal” algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the "ideal" algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.

Helping someone with problem drinking: Mental health first aid guidelines - a Delphi expert consensus study

Background Alcohol is a leading risk factor for avoidable disease burden. Research suggests that a drinker's social network can play an integral role in addressing hazardous (i.e., high-risk) or problem drinking. Often however, social networks do not have adequate mental health literacy (i.e., knowledge about mental health problems, like problem drinking, or how to treat them). This is a concern as the response that a drinker receives from their social network can have a substantial impact on their willingness to seek help. This paper describes the development of mental health first aid guidelines that inform community members on how to help someone who may have, or may be developing, a drinking problem (i.e., alcohol abuse or dependence). Methods A systematic review of the research and lay literature was conducted to develop a 285-item survey containing strategies on how to help someone who may have, or may be developing, a drinking problem. Two panels of experts (consumers/carers and clinicians) individually rated survey items, using a Delphi process. Surveys were completed online or via postal mail. Participants were 99 consumers, carers and clinicians with experience or expertise in problem drinking from Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States. Items that reached consensus on importance were retained and written into guidelines. Results The overall response rate across all three rounds was 68.7% (67.6% consumers/carers, 69.2% clinicians), with 184 first aid strategies rated as essential or important by ≥80% of panel members. The endorsed guidelines provide guidance on how to: recognize problem drinking; approach someone if there is concern about their drinking; support the person to change their drinking; respond if they are unwilling to change their drinking; facilitate professional help seeking and respond if professional help is refused; and manage an alcohol-related medical emergency. Conclusion The guidelines provide a consensus-based resource for community members seeking to help someone with a drinking problem. Improving community awareness and understanding of how to identify and support someone with a drinking problem may lead to earlier recognition of problem drinking and greater facilitation of professional help seeking.

Expert science teacher's notion of scientific literacy

This paper will report on the way expert science teachers’ conceive of scientific literacy in their classrooms, the values related to scientific literacy they hold and how this conception and the underpinning values affect their teaching practice. Three perceived expert science teachers who teach both at senior and middle school levels and across the range of sub-disciplines (one senior biology, one senior chemistry and one senior physics) were interviewed about their understanding of scientific literacy and how this influenced their teaching practice. The three teachers were video recorded teaching a junior science class and a senior science class. The data were analysed to identify values that underpin their conceptions of science and science education. The analysis focussed on the matching of the verbalised conceptions and values with their practice of teaching science. This paper will report on these data.

Expert elicitation into Bayesian networks : understanding probability

An introduction to thinking about and understanding probability that highlights the main pits and trapfalls that befall logical reasoning

Expert elicitation into Bayesian networks, Part 1 of 4 : understanding probability

An introduction to elicitation of experts' probabilities, which illustrates common problems with reasoning and how to circumvent them during elicitation.

Expert elicitation into Bayesian networks, part 2 of 4: designing elicitation

An introduction to design of eliciting knowledge from experts.

Expert elicitation into Bayesian networks, Part 4 of 4: eliciting a conditional probability table

An introduction to eliciting a conditional probability table in a Bayesian Network model, highlighting three efficient methods for populating a CPT.

Developing a framework for Six Sigma in financial service institutions : empirical evidence from expert interviews

Six Sigma is considered to be an important management philosophy to obtain satisfied customers. But financial service organisations have been slow to adopt Six Sigma issues so far. Despite the extensive effort that has been invested and benefits that can be obtained, the systematic implementation of Six Sigma in financial service organisations is limited. As a company wide implementation framework is missing so far, this paper tries to fill this gap. Based on theory, a conceptual framework is developed and evaluated by experts from financial institutions. The results show that it is very important to link Six Sigma with the strategic as well as the operations level. Furthermore, although Six Sigma is a very important method for improving quality of processes others such as Lean Management are also used This requires a superior project portfolio management to coordinate resources and projects of Six Sigma with the other methods used. Beside the theoretical contribution, the framework can be used by financial service companies to evaluate their Six Sigma activities. Thus, the framework grounded through literature and empirical data will be a useful guide for sustainable and successful implementation of a Six Sigma initiative in financial service organisations.

A proposed validation framework for expert elicited Bayesian Networks

The popularity of Bayesian Network modelling of complex domains using expert elicitation has raised questions of how one might validate such a model given that no objective dataset exists for the model. Past attempts at delineating a set of tests for establishing confidence in an entirely expert-elicited model have focused on single types of validity stemming from individual sources of uncertainty within the model. This paper seeks to extend the frameworks proposed by earlier researchers by drawing upon other disciplines where measuring latent variables is also an issue. We demonstrate that even in cases where no data exist at all there is a broad range of validity tests that can be used to establish confidence in the validity of a Bayesian Belief Network.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

Eliciting Expert Knowledge in Conservation Science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.