Developing a framework for planning healthy communities : the Logan Beaudesert health decision support system

In the last few decades, the focus on building healthy communities has grown significantly (Ashton, 2009). There is growing evidence that new approaches to planning are required to address the challenges faced by contemporary communities. These approaches need to be based on timely access to local information and collaborative planning processes (Murray, 2006; Scotch & Parmanto, 2006; Ashton, 2009; Kazda et al., 2009). However, there is little research to inform the methods that can support this type of responsive, local, collaborative and consultative health planning (Northridge et al., 2003). Some research justifies the use of decision support systems (DSS) as a tool to support planning for healthy communities. DSS have been found to increase collaboration between stakeholders and communities, improve the accuracy and quality of the decision-making process, and improve the availability of data and information for health decision-makers (Nobre et al., 1997; Cromley & McLafferty, 2002; Waring et al., 2005). Geographic information systems (GIS) have been suggested as an innovative method by which to implement DSS because they promote new ways of thinking about evidence and facilitate a broader understanding of communities. Furthermore, literature has indicated that online environments can have a positive impact on decision-making by enabling access to information by a broader audience (Kingston et al., 2001). However, only limited research has examined the implementation and impact of online DSS in the health planning field. Previous studies have emphasised the lack of effective information management systems and an absence of frameworks to guide the way in which information is used to promote informed decisions in health planning. It has become imperative to develop innovative approaches, frameworks and methods to support health planning. Thus, to address these identified gaps in the knowledge, this study aims to develop a conceptual planning framework for creating healthy communities and examine the impact of DSS in the Logan Beaudesert area. Specifically, the study aims to identify the key elements and domains of information that are needed to develop healthy communities, to develop a conceptual planning framework for creating healthy communities, to collaboratively develop and implement an online GIS-based Health DSS (i.e., HDSS), and to examine the impact of the HDSS on local decision-making processes. The study is based on a real-world case study of a community-based initiative that was established to improve public health outcomes and promote new ways of addressing chronic disease. The study involved the development of an online GIS-based health decision support system (HDSS), which was applied in the Logan Beaudesert region of Queensland, Australia. A planning framework was developed to account for the way in which information could be organised to contribute to a healthy community. The decision support system was developed within a unique settings-based initiative Logan Beaudesert Health Coalition (LBHC) designed to plan and improve the health capacity of Logan Beaudesert area in Queensland, Australia. This setting provided a suitable platform to apply a participatory research design to the development and implementation of the HDSS. Therefore, the HDSS was a pilot study examined the impact of this collaborative process, and the subsequent implementation of the HDSS on the way decision-making was perceived across the LBHC. As for the method, based on a systematic literature review, a comprehensive planning framework for creating healthy communities has been developed. This was followed by using a mixed method design, data were collected through both qualitative and quantitative methods. Specifically, data were collected by adopting a participatory action research (PAR) approach (i.e., PAR intervention) that informed the development and conceptualisation of the HDSS. A pre- and post-design was then used to determine the impact of the HDSS on decision-making. The findings of this study revealed a meaningful framework for organising information to guide planning for healthy communities. This conceptual framework provided a comprehensive system within which to organise existing data. The PAR process was useful in engaging stakeholders and decision-making in the development and implementation of the online GIS-based DSS. Through three PAR cycles, this study resulted in heightened awareness of online GIS-based DSS and openness to its implementation. It resulted in the development of a tailored system (i.e., HDSS) that addressed the local information and planning needs of the LBHC. In addition, the implementation of the DSS resulted in improved decision- making and greater satisfaction with decisions within the LBHC. For example, the study illustrated the culture in which decisions were made before and after the PAR intervention and what improvements have been observed after the application of the HDSS. In general, the findings indicated that decision-making processes are not merely informed (consequent of using the HDSS tool), but they also enhance the overall sense of ‗collaboration‘ in the health planning practice. For example, it was found that PAR intervention had a positive impact on the way decisions were made. The study revealed important features of the HDSS development and implementation process that will contribute to future research. Thus, the overall findings suggest that the HDSS is an effective tool, which would play an important role in the future for significantly improving the health planning practice.

Social ties as key knowledge transfer agents between Indonesian and Australian universities : Perspectives from an Indonesian university

As two neighbouring countries, the number of transnational programs between Indonesian and Australian universities is significant. However, little is known about how transnational programs can facilitate knowledge transfer between the partner universities, which is often assumed by the Indonesian universities. Based on a case study regarding a dual degree program between an Indonesian and an Australian university, this paper outlines preliminary findings concerning the role of social ties between the staff of the two partner universities in creating positive inter-university dynamics that is vital for successful knowledge transfer. Using an inter-organisational knowledge transfer theoretical framework, social ties between the staff of the two universities are viewed as an important agent in facilitating knowledge transfer by building trust between the partners, moderating the perception about risk in the partnership, and creating a more equal power relation between the universities. Based on this study, Australian lecturers of Indonesian background and Indonesian lecturers who are alumni of Australian universities are important to initially establish these social ties. While face-to-face contact is still perceived as the ideal means of transferring knowledge and building trust among the Indonesian university staff, those who have stronger social ties with their Australian counterparts tend to use ICT-based communication to acquire knowledge from the Australian university compared to those who have more limited social ties with their Australian counterparts. This paper concludes with some implications for building positive social ties between Indonesian and Australian university staff to strengthen the knowledge transfer process.

Using Wikipedia to improve web service discovery

Building and maintaining software are not easy tasks. However, thanks to advances in web technologies, a new paradigm is emerging in software development. The Service Oriented Architecture (SOA) is a relatively new approach that helps bridge the gap between business and IT and also helps systems remain exible. However, there are still several challenges with SOA. As the number of available services grows, developers are faced with the problem of discovering the services they need. Public service repositories such as Programmable Web provide only limited search capabilities. Several mechanisms have been proposed to improve web service discovery by using semantics. However, most of these require manually tagging the services with concepts in an ontology. Adding semantic annotations is a non-trivial process that requires a certain skill-set from the annotator and also the availability of domain ontologies that include the concepts related to the topics of the service. These issues have prevented these mechanisms becoming widespread. This thesis focuses on two main problems. First, to avoid the overhead of manually adding semantics to web services, several automatic methods to include semantics in the discovery process are explored. Although experimentation with some of these strategies has been conducted in the past, the results reported in the literature are mixed. Second, Wikipedia is explored as a general-purpose ontology. The benefit of using it as an ontology is assessed by comparing these semantics-based methods to classic term-based information retrieval approaches. The contribution of this research is significant because, to the best of our knowledge, a comprehensive analysis of the impact of using Wikipedia as a source of semantics in web service discovery does not exist. The main output of this research is a web service discovery engine that implements these methods and a comprehensive analysis of the benefits and trade-offs of these semantics-based discovery approaches.

Distributing leadership and cultivating dialogue with collaborative EBIP

The purpose of this paper is to demonstrate the efficacy of collaborative evidence based information practice (EBIP) as an organizational effectiveness model. Shared leadership, appreciative inquiry and knowledge creation theoretical frameworks provide the foundation for change toward the implementation of a collaborative EBIP workplace model. Collaborative EBIP reiterates the importance of gathering the best available evidence, but it differs by shifting decision-making authority from "library or employer centric" to "user or employee centric". University of Colorado Denver Auraria Library Technical Services department created a collaborative EBIP environment by flattening workplace hierarchies, distributing problem solving and encouraging reflective dialogue. By doing so, participants are empowered to identify problems, create solutions, and become valued and respected leaders and followers. In an environment where library budgets are in jeopardy, recruitment opportunities are limited and the workplace is in constant flux, the Auraria Library case study offers an approach that maximizes the capability of the current workforce and promotes agile responsiveness to industry and organizational challenges. Collaborative EBIP is an organizational model demonstrating a process focusing first on the individual and moving to the collective to develop a responsive and high performing business unit, and in turn, organization.

CoRE : a context-aware relation extraction method for relation completion

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation, RC attempts at linking entity pairs between two entity lists under the relation. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

Co-creating stories in social learning systems : The role of community media and cultural organisations in disseminating knowledge

This paper investigates how community based media organisations are co-creative storytelling institutions, and how they learn to disseminate knowledge in a social learning system. Organisations involved in story co-creation are learning to create in fluid environments.They are project based, with a constant turnover of volunteers or staff. These organisations have to meet the needs of their funding bodies and their communities to remain sustainable. Learning is seen as dialogical, and this is also reflected in the nature of storytelling itself. These organisations must learn to meet the needs of their communities, who in turn learn from the organisation’s expertise in a facilitated setting. This learning is participatory and collaborative, and is often a mix of virtual and offline interaction. Such community-based organisations sit in the realm of a hybrid-learning environment; they are neither a formal educational institution like a college, nor do their volunteers produce outcomes in a professional capacity. Yet, they must maintain a certain level of quality outcomes from their contributors to be of continued value in their communities. Drawing from a larger research study, one particular example is that of the CitizenJ project. CitizenJ is hosted by a state cultural centre, and partnered with publishing partners in the community broadcasting sector. This paper explores how this project is a Community of Practice, and how it promotes ethical and best practice, meets contributors’ needs, emphasises the importance of facilitation in achieving quality outcomes, and the creation of projects for wider community and public interest.

Educating cultural heritage information professionals for Australia's galleries, libraries, archives and museums: A grounded Delphi study

This research explored the knowledge, skills, qualities, and professional education needs, of information professionals in galleries, libraries, archives and museums (GLAM) in Australia. The findings revealed that although full convergence of these sectors is unlikely, many of the skills, knowledge and qualities would be required across all four sectors. The research used the Grounded Delphi Method, a relatively new methodological extension of the Delphi method that incorporates aspects of Grounded Theory. The findings provide the first empirically based guidelines around what needs to be included in an educational framework for information professionals who will work in the emerging GLAM environment. As the first study of GLAM education requirements in Australia and the wider Asia-Pacific region to take a holistic approach by engaging information professionals across all four sectors, this thesis makes a contribution to the GLAM research field and to information education generally.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Investigating the use of association rules in improving recommender systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus when there is not sufficient knowledge on a user it is difficult for a recommender system to make quality recommendations. This problem is often referred to as the cold-start problem. Here we investigate whether association rules can be used as a source of information to expand a user profile and thus avoid this problem, leading to improved recommendations to users. Our pilot study shows that indeed it is possible to use association rules to improve the performance of a recommender system. This we believe can lead to further work in utilising appropriate association rules to lessen the impact of the cold-start problem.

An analysis of cyclists crashes to identify ITS-based intervention

Cycling provides a number of health and environmental benefits. However, cyclists are more likely to suffer serious injury or be killed in traffic accidents than car drivers and the estimated cost of crashes in Australia is $1.25AU billion per year. Current interventions to reduce bicycle crashes include compulsory helmet use, media campaigns, and the provision of cycling lanes, as well as road user education and training. It is difficult to assess the effectiveness of current interventions as there is no accurate measure of cyclist exposure in South East Queensland (SEQ). This paper analyses cyclist crash characteristics in Queensland with the view to identifying appropriate Intelligent Transport Systems (ITS) based intervention to reduce cyclist injury and death. The inappropriateness of some ITS interventions to improve cyclist safety is highlighted and a set of ITS interventions are identified, based on Queensland crash data 2002-2006.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Tailoring Consumer Resources to Enhance Self-Care in Chronic Heart Failure

Background Chronic heart failure (CHF) is associated with high hospitalisation and mortality rates and debilitating symptoms. In an effort to reduce hospitalisations and improve symptoms individuals must be supported in managing their condition. Patients who can effectively self-manage their symptoms through lifestyle modification and adherence to complex medication regimens will experience less hospitalisations and other adverse events. Aim The purpose of this paper is to explain how providing evidence-based information, using patient education resources, can support self-care. Discussion Self-care relates to the activities that individuals engage in relation to health seeking behaviours. Supporting self-care practices through tailored and relevant information can provide patients with resources and advice on strategies to manage their condition. Evidence-based approaches to improve adherence to self-care practices in patients with heart failure are not often reported. Low health literacy can result in poor understanding of the information about CHF and is related to adverse health outcomes. Also a lack of knowledge can lead to non-adherence with self-care practices such as following fluid restriction, low sodium diet and daily weighing routines. However these issues need to be addressed to improve self-management skills. Outcome Recently the Heart Foundation CHF consumer resource was updated based on evidence-based national clinical guidelines. The aim of this resource is to help consumers improve understanding of the disease, reduce uncertainty and anxiety about what to do when symptoms appear, encourage discussions with local doctors, and build confidence in self-care management. Conclusion Evidence-based CHF patient education resources promote self-care practices and early detection of symptom change that may reduce hospitalisations and improve the quality of life for people with CHF.

Information interventions for orienting patients and their carers to cancer care facilities

Background: Cancer patients experience distress and anxiety related to their diagnosis, treatment and the unfamiliar cancer centre. Strategies with the aim of orienting patients to a cancer care facility may improve patient outcomes. Although meeting patients' information needs at different stages is important, there is little agreement about the type of information and the timing for information to be given. Orientation interventions aim to address information needs at the start of a person's experience with a cancer care facility. The extent of any benefit of these interventions is unknown. Objectives: To assess the effects of information interventions which orient patients and their carers/family to a cancer care facility, and to the services available in the facility. Search Methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2011, Issue 2); MEDLINE (OvidSP) (1966 to Jun 2011), EMBASE (Ovid SP) (1966 to Jun 2011), CINAHL (EBSCO) (1982 to Jun 2011), PsycINFO (OvidSP) (1966 to Jun 2011), review articles and reference lists of relevant articles. We contacted principal investigators and experts in the field. Selection Criteria: Randomised controlled trials (RCTs), cluster RCTs and quasi-RCTs evaluating the effects of information interventions that orient patients and their carers/family to a cancer care facility. Data collection and analysis: Results of searches were reviewed against the pre-determined criteria for inclusion by two review authors. The primary outcomes were knowledge and understanding; health status and wellbeing, evaluation of care, and harms. Secondary outcomes were communication, skills acquisition, behavioural outcomes, service delivery, and health professional outcomes. We pooled results of RCTs using mean differences (MD) and 95% confidence intervals (CI). Main results: We included four RCTs involving 610 participants. All four trials aimed to investigate the effects of orientation programs for cancer patients to a cancer facility. There was high risk of bias across studies. Findings from two of the RCTs demonstrated significant benefits of the orientation intervention in relation to levels of distress (mean difference (MD) -8.96 (95% confidence interval (CI) -11.79 to -6.13), but non-significant benefits in relation to state anxiety levels (MD -9.77 (95% CI -24.96 to 5.41). Other outcomes for participants were generally positive (e.g. more knowledgeable about the cancer centre and cancer therapy, better coping abilities). No harms or adverse effects were measured or reported by any of the included studies. There were insufficient data on the other outcomes of interest. Authors conclusion: This review has demonstrated the feasibility and some potential benefits of orientation interventions. There was a low level of evidence suggesting that orientation interventions can reduce distress in patients. However, most of the other outcomes remain inconclusive (patient knowledge recall/ satisfaction). The majority of studies were subject to high risk of bias, and were likely to be insufficiently powered. Further well conducted and powered RCTs are required to provide evidence for determining the most appropriate intensity, nature, mode and resources for such interventions. Patient and carer-focused outcomes should be included.

A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

Having a good automatic anomalous human behaviour detection is one of the goals of smart surveillance systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.