Beyond competencies : using a capability framework in developing practice standards for advanced practice nursing

AIM This paper presents a discussion on the application of a capability framework for advanced practice nursing standards/competencies. BACKGROUND There is acceptance that competencies are useful and necessary for definition and education of practice-based professions. Competencies have been described as appropriate for practice in stable environments with familiar problems. Increasingly competencies are being designed for use in the health sector for advanced practice such as the nurse practitioner role. Nurse practitioners work in environments and roles that are dynamic and unpredictable necessitating attributes and skills to practice at advanced and extended levels in both familiar and unfamiliar clinical situations. Capability has been described as the combination of skills, knowledge, values and self-esteem which enables individuals to manage change, be flexible and move beyond competency. DESIGN A discussion paper exploring 'capability' as a framework for advanced nursing practice standards. DATA SOURCES Data were sourced from electronic databases as described in the background section. IMPLICATIONS FOR NURSING As advanced practice nursing becomes more established and formalized, novel ways of teaching and assessing the practice of experienced clinicians beyond competency are imperative for the changing context of health services. CONCLUSION Leading researchers into capability in health care state that traditional education and training in health disciplines concentrates mainly on developing competence. To ensure that healthcare delivery keeps pace with increasing demand and a continuously changing context there is a need to embrace capability as a framework for advanced practice and education.

A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an R2 goodness of fit of 0.9994 and 0.9982 respectively over a 10 h test period. The utility of the framework is demonstrated on a number of usage scenarios including causal analysis and ‘what-if’ analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

A framework for model integration and holistic modelling of socio-technical systems

This paper presents a layered framework for the purposes of integrating different Socio-Technical Systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another is obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.

A Likelihood-Maximizing Framework for Enhanced In-Car Speech Recognition Based on Speech Dialog System Interaction

Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating noncritical in-car systems. Under such conditions, however, speech recognition accuracy degrades significantly, and techniques such as speech enhancement are required to improve these accuracies. Likelihood-maximizing (LIMA) frameworks optimize speech enhancement algorithms based on recognized state sequences rather than traditional signal-level criteria such as maximizing signal-to-noise ratio. LIMA frameworks typically require calibration utterances to generate optimized enhancement parameters that are used for all subsequent utterances. Under such a scheme, suboptimal recognition performance occurs in noise conditions that are significantly different from that present during the calibration session – a serious problem in rapidly changing noise environments out on the open road. In this chapter, we propose a dialog-based design that allows regular optimization iterations in order to track the ever-changing noise conditions. Experiments using Mel-filterbank noise subtraction (MFNS) are performed to determine the optimization requirements for vehicular environments and show that minimal optimization is required to improve speech recognition, avoid over-optimization, and ultimately assist with semireal-time operation. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session only.

A framework for sustainable energy reduction in modern breweries

This thesis examines the existing frameworks for energy management in the brewing industry and details the design, development and implementation of a new framework at a modern brewery. The aim of the research was to develop an energy management framework to identify opportunities in a systematic manner using Systems Engineering concepts and principles. This work led to a Sustainable Energy Management Framework, SEMF. Using the SEMF approach, one of Australia's largest breweries has achieved number 1 ranking in the world for water use for the production of beer and has also improved KPI's and sustained the energy management improvements that have been implemented during the past 15 years. The framework can be adapted to other manufacturing industries in the Australian context and is considered to be a new concept and a potentially important tool for energy management.