The development of a theory-based intervention to promote appropriate disclosure of a diagnosis of dementia

Background: The development and description of interventions to change professional practice are often limited by the lack of an explicit theoretical and empirical basis. We set out to develop an intervention to promote appropriate disclosure of a diagnosis of dementia based on theoretical and empirical work. Methods: We identified three key disclosure behaviours: finding out what the patient already knows or suspects about their diagnosis; using the actual words 'dementia' or 'Alzheimer's disease' when talking to the patient; and exploring what the diagnosis means to the patient. We conducted a questionnaire survey of older peoples' mental health teams (MHTs) based upon theoretical constructs from the Theory of Planned Behaviour (TPB) and Social Cognitive Theory (SCT) and used the findings to identify factors that predicted mental health professionals' intentions to perform each behaviour. We selected behaviour change techniques likely to alter these factors. Results: The change techniques selected were: persuasive communication to target subjective norm; behavioural modelling and graded tasks to target self-efficacy; persuasive communication to target attitude towards the use of explicit terminology when talking to the patient; and behavioural modelling by MHTs to target perceived behavioural control for finding out what the patient already knows or suspects and exploring what the diagnosis means to the patient. We operationalised these behaviour change techniques using an interactive 'pen and paper' intervention designed to increase intentions to perform the three target behaviours. Conclusion : It is feasible to develop an intervention to change professional behaviour based upon theoretical models, empirical data and evidence based behaviour change techniques. The next step is to evaluate the effect of such an intervention on behavioural intention. We argue that this approach to development and reporting of interventions will contribute to the science of implementation by providing replicable interventions that illuminate the principles and processes underlying change.This project is funded by UK Medical Research Council, Grant reference number G0300999. Jeremy Grimshaw holds a Canada Research Chair in Health Knowledge Transfer and Uptake. Jill Francis is funded by the Chief Scientist Office of the Scottish Government Health Directorate. The views expressed in this study are those of the authors

Risk Management in the Arctic Offshore: Wicked Problems Require New Paradigms

Recent project-management literature and high-profile disasters—the financial crisis, the BP Deepwater Horizon oil spill, and the Fukushima nuclear accident—illustrate the flaws of traditional risk models for complex projects. This research examines how various groups with interests in the Arctic offshore define risks. The findings link the wicked problem framework and the emerging paradigm of Project Management of the Second Order (PM-2). Wicked problems are problems that are unstructured, complex, irregular, interactive, adaptive, and novel. The authors synthesize literature on the topic to offer strategies for navigating wicked problems, provide new variables to deconstruct traditional risk models, and integrate objective and subjective schools of risk analysis

Implementation of QoS onto virtual bus network

Quality of Service (QoS) is a key issue in a multimedia environment because multimedia applications are sensitive to delay. The virtual bus architecture is a hierarchical access network structure that has been proposed to simplify network signaling. The network employs an interconnection of hierarchical database to support advanced routing of the signaling and traffic load. Therefore, the requirements and management of quality of service is important in the virtual bus network particularly to support multimedia applications. QoS and traffic parameters are specified for each class type and the OMNeT model has been described

Farming profitably in a changing climate: a risk management approach

Climate science has made enormous progress over the last two decades in understanding the nature of earth's climate and the changes that are taking place. Under climate change projections, we can say with some confidence that the Australian climate will continue to become hotter, and temperature-related extreme events are likely to increase in frequency. However, we cannot yet project with any reasonable level of confidence changes to rainfall and the occurrence of drought. So although there is strong evidence for the reality of climate change, there is still considerable uncertainty associated with projections of precisely how climate change will unfold in the future, particularly at regional and local scales where most farming management decisions are made. Adapting to such an uncertain future demands a flexible approach based on assessing, analysing and responding to the risks posed by a changing climate. This paper examines a risk management approach to farming in a variable and changing climate, based on experience gained in the insurance industry which is one of the first major industries to be impacted by climate change losses. Governments, businesses and individuals must consider the implications of a variable and changing climate as a normal part of decision-making based on risk, just as they would for other risks, such as market price and fuel price movements, labour costs etc. The paper also discusses briefly how advances in information technology have enabled information to be accessed and widely distributed, and showcases four best practice spatial IT website tools developed by the BRS to assist farmers and policy makers to manage risk - the National Agricultural Monitoring System (NAMS), the Meat and Livestock Australia (MLA) Rainfall to Pasture Growth Outlook Tool, the Multi-Criteria Analysis Shell (MCAS-S), and the Rainfall Reliability Wizard. There are also several tools under current development in BRS which continue with this theme. These are Water 2010 - National Water Balance and Information for Policy and Planning, the Climate Change Wizard and Climate Change Impacted Data Sets.Climate change, risk management, Environmental Economics and Policy, Risk and Uncertainty,

The management of intelligence-assisted finite element analysis technology

Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA

Impact in networks and ecosystems: building case studies that make a difference

open accessThis toolkit aims to support the building up of case studies that show the impact of project activities aiming to promote innovation and entrepreneurship. The case studies respond to the challenge of understanding what kinds of interventions work in the Southern African region, where, and why. The toolkit has a specific focus on entrepreneurial ecosystems and proposes a method of mapping out the actors and their relationships over time. The aim is to understand the changes that take place in the ecosystems. These changes are seen to be indicators of impact as increased connectivity and activity in ecosystems are key enablers of innovation. Innovations usually happen together with matching social and institutional adjustments, facilitating the translation of inventions into new or improved products and services. Similarly, the processes supporting entrepreneurship are guided by policies implemented in the common framework provided by innovation systems. Overall, policies related to systems of innovation are by nature networking policies applied throughout the socioeconomic framework of society to pool scarce resources and make various sectors work in coordination with each other. Most participating SAIS countries already have some kinds of identifiable systems of innovation in place both on national and regional levels, but the lack of appropriate institutions, policies, financial instruments, human resources, and support systems, together with underdeveloped markets, create inefficiencies and gaps in systemic cooperation and collaboration. In other words, we do not always know what works and what does not. On another level, engaging users and intermediaries at the local level and driving the development of local innovation ecosystems within which local culture, especially in urban settings, has evident impact on how collaboration and competition is both seen and done. In this complex environment, organisations supporting entrepreneurship and innovation often find it difficult to create or apply relevant knowledge and appropriate networking tools, approaches, and methods needed to put their processes to work for broader developmental goals. To further enable these organisations’ work, it is necessary to understand what works and why in a given environment. Enhanced local and regional cooperation promoted by SAIS Innovation Fund projects can generate new data on this little-explored area in Southern Africa. Data-driven knowledge on entrepreneurship and innovation support best practices as well as effective and efficient management of entrepreneurial ecosystems can support replication and inform policymaking, leading thus to a wider impact than just that of the immediate reported projects and initiatives

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page