Is public health policy based on scholarly evidence? With the manifold variables that policy makers must consider, is evidence-based policy even realistic? While strategies exist to translate research into policy, a need to understand better how that can play out in real-life remains. Using interviews from informants occupying a range of positions, and considering the atmosphere created by media reports, this study examines the case of smoking privileges at East Coast Forensic Hospital. After a patient committed murder while on leave, apparently to smoke, public pressure over public safety, a relative lack of relevant scholarship, ethical considerations, and the divergent voices of stakeholders created challenging circumstances for policy makers. Through the use of case study methodology, this project identifies the kinds of information that are employed in the creation or modification of policy and offers insights concerning how the influences exerted on policy makers determine how information is employed.

Thesis (Ph.D.)--University of Wollongong, 2005. / Typescript. Includes appendices. Bibliographical references: leaf 175-181.

Univ. Fridericiana, Fak. für Wirtschaftswissenschaften, Diss.--Karlsruhe, 2006.

Thesis (Ph.D.)--University of Wollongong, 2007. / Typescript. Includes bibliographical references: page 175-183.

Thesis (M.Agric.) - University of KwaZulu-Natal, PIetermaritzburg, 2007. / Full text also available online. Scroll down for electronic link.

Thesis (MPhil)--University of Stellenbosch, 2005. / On title page: Master of Philosophy (Information and Knowledge Management). Bibliography. Also available via the Internet.

abstract: Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or low-level data entities. Also, additional domain knowledge may often be indispensable for uncovering the underlying semantics, but in most cases such domain knowledge is not readily available from the acquired media streams. Thus, making use of various types of contextual information and leveraging corresponding domain knowledge are vital for effectively associating high-level semantics with low-level signals with higher accuracies in multimedia computing problems. In this work, novel computational methods are explored and developed for incorporating contextual information/domain knowledge in different forms for multimedia computing and pattern recognition problems. Specifically, a novel Bayesian approach with statistical-sampling-based inference is proposed for incorporating a special type of domain knowledge, spatial prior for the underlying shapes; cross-modality correlations via Kernel Canonical Correlation Analysis is explored and the learnt space is then used for associating multimedia contents in different forms; model contextual information as a graph is leveraged for regulating interactions among high-level semantic concepts (e.g., category labels), low-level input signal (e.g., spatial/temporal structure). Four real-world applications, including visual-to-tactile face conversion, photo tag recommendation, wild web video classification and unconstrained consumer video summarization, are selected to demonstrate the effectiveness of the approaches. These applications range from classic research challenges to emerging tasks in multimedia computing. Results from experiments on large-scale real-world data with comparisons to other state-of-the-art methods and subjective evaluations with end users confirmed that the developed approaches exhibit salient advantages, suggesting that they are promising for leveraging contextual information/domain knowledge for a wide range of multimedia computing and pattern recognition problems. / Dissertation/Thesis / Ph.D. Computer Science 2011

Knowledge sharing and information security have become well-established concepts in academia and within organisations. Knowledge sharing aims to encourage individuals to share tacit and explicit knowledge with colleagues and stakeholders, yet on the other hand, information security initiatives aim to apply controls and restrictions to the knowledge that can be shared and how it can be shared, where the primary focus is usually on protecting explicit knowledge or information. This thesis draws attention to the largely unexplored and under-developed area of knowledge protection ; it investigates the paradoxical and concurrent nature of knowledge sharing and information security practices by exploring their relationship and understanding how this can affect an organisation and subsequently identifies ways of achieving a balance between the two practices. The empirical work was carried out through an interpretivist case study approach in the Energy Technologies Institute (ETI) an organisation that combines knowledge and expertise from partnerships with academia, industry and the UK government, in order to deliver innovative low carbon solutions. A novel team-based action learning approach was developed to generate individual, team and organisational learning and to help initiate change; the data was collected from three project teams about their knowledge and experiences of knowledge sharing and information security practices, which was then analysed and further supplemented with the ETI s organisational perspective and the researcher s own experience of collaborating with the ETI to contextualise the findings. Eight predominant overarching themes were identified that play an important role in and influence the organisation s knowledge sharing and information security practices. When looking at the practices of knowledge sharing and information security independently at the ETI, proactive and conscious efforts towards achieving the goals of each practice are evident. Knowledge is recognised as the ETI s core product and its effective dissemination is key for the organisation s success, which is why there is a keen attitude towards improving knowledge sharing internally and externally. On the other hand, a great deal of importance is given to protecting valuable knowledge and meeting stakeholders confidentiality requirements, thus, there are good systems, access controls, and information restrictions in place. In addition, strict legal and approval processes to protect information value and accuracy are implemented. However, when both knowledge sharing and information security - practices are compared from a broader perspective, evidence of issues arising from their conflicting nature is evident. Moreover, operating in a complex governance structure with various expectations and contractual agreements with stakeholders regarding confidentiality, has created a protective culture in the organisation surrounding its knowledge, which causes a hindrance to formal and informal knowledge sharing (including both, tacit and explicit forms) and makes identifying opportunities for fully exploiting knowledge and Intellectual Property an ongoing operational challenge. The research process facilitated the achievement of effective learning at individual, team and organisational level for the ETI about its practices, identification of challenges and areas of improvement, incorporation of learning and recommendations into its knowledge management strategy alongside existing activities to improve knowledge sharing. The contents of this thesis particularly the eight themes that have emerged from the research findings - are also contributing significantly to a project the organisation is carrying out to reflect on and review what has been learned from operating the ETI for the last 10 years. The thesis contributes to the existing body of knowledge, theoretically and practically, in the disciplines of knowledge management and information security; what was predominantly overlooked by previous literature, the empirical research findings surface evidence of the relationship between knowledge sharing and information security practices, showing their interconnectedness, and, the negative consequences of the two practices being treated and managed separately. For the action learning arena, a novel methodological approach underpinned by the action learning philosophy has been introduced that demonstrates how team action learning (i.e. using intact teams as opposed to conventional action learning teams) can be used to engage employees to share and combine their knowledge on real organisational issues, generate new learning and develop actions to initiate improvements in the organisation.

In recent years nearly every aspect of how we function as a society has transformed from analogue to digital. This has spurred extraordinary change and acted as a catalyst for technology innovation, as well as big data generation. Big data is characterized by its constantly growing volume, wide variety, high velocity, and powerful veracity. With the emergence of COVID-19, the global pandemic has demonstrated the profound impact, and often dangerous consequences, when communicating health information derived from data. Healthcare companies have access to enormous data assets, yet communicating information from their data sources is complex as they also operate in one of the most highly regulated business environments where data privacy and legal requirements vary significantly from one country to another. The purpose of this study is to understand how global healthcare companies communicate information derived from data to their internal and external audiences. The research proposes a model for how marketing communications, public relations, and internal communications practitioners can address the challenges of utilizing data in communications in order to advance organizational priorities and achieve business goals. The conceptual framework is based on a closed-loop communication flow and includes an encoding process specialized for incorporating big data into communications. The results of the findings reveal tactical communication strategies, as well as organizational and managerial practices that can position practitioners best for communicating big data. The study concludes by proposing recommendations for future research, particularly from interdisciplinary scholars, to address the research gaps.

The Semantic Web has ushered in a vast repository of openly available data across various domains, resulting in over ten thousand Knowledge Graphs (KGs) published under the Linked Open Data (LOD) cloud. However, the exploration of these KGs can be time-consuming and resource-intensive, compounded by issues of availability and duplication across distributed and decentralized databases. Addressing these challenges, this thesis investigates methods for improving information retrieval over Linked Data (LD) through conceptual approaches facilitating access via formal and natural language queries. First, RDFSlice is introduced to efficiently select relevant fragments of RDF data from distributed KGs, demonstrating superior performance compared to conventional methods. Second, a novel distributed and decentralized publishing architecture is proposed to simplify data sharing and querying, enhancing reliability and efficiency. Third, a benchmark for evaluating ranking functions for RDF data is created, leading to the development of new ranking functions such as DBtrends and MIXED-RANK. Fourth, a scoring function based on Term Networks is proposed for interpreting factual queries, outperforming traditional information retrieval methods. Lastly, user interface patterns are discussed, and an extension for semantic search is proposed to improve information access in the face of the vast amounts of data available on the LOD cloud. These contributions collectively address key challenges in accessing and utilizing RDF data, offering insights and solutions to facilitate efficient information retrieval and exploration in the Semantic Web era.