Medical nutrition therapy and simple interventions can improve intake in patients who eat poorly in hospital

The Australasian Nutrition Care Day Survey (ANCDS) reported two-in-five patients in Australian and New Zealand hospitals consume ≤50% of the offered food. The ANCDS found a significant association between poor food intake and increased in-hospital mortality after controlling for confounders (nutritional status, age, disease type and severity)1. Evidence for the effectiveness of medical nutrition therapy (MNT) in hospital patients eating poorly is lacking. An exploratory study was conducted in respiratory, neurology and orthopaedic wards of an Australian hospital. At baseline, 24-hour food intake (0%, 25%, 50%, 75%, 100% of offered meals) was evaluated for patients hospitalised for ≥2 days and not under dietetic review. Patients consuming ≤50% of offered meals due to nutrition-impact symptoms were referred to ward dietitians for MNT with food intake re-evaluated on day-7. 184 patients were observed over four weeks. Sixty-two patients (34%) consumed ≤50% of the offered meals. Simple interventions (feeding/menu assistance, diet texture modifications) improved intake to ≥75% in 30 patients who did not require further MNT. Of the 32 patients referred for MNT, baseline and day-7 data were available for 20 patients (68±17years, 65% females, BMI: 22±5kg/m2, median energy, protein intake: 2250kJ, 25g respectively). On day-7, 17 participants (85%) demonstrated significantly higher consumption (4300kJ, 53g; p<0.01). Three participants demonstrated no improvement due to ongoing nutrition-impact symptoms. “Percentage food intake” was a quick tool to identify patients in whom simple interventions could enhance intake. MNT was associated with improved dietary intake in hospital patients. Further research is needed to establish a causal relationship.

The effect of polystyrene sodium sulfonate grafting on polyethylene terephthalate artificial ligaments on in vitro mineralisation and in vivo bone tissue integration

This study investigates the impact of polystyrene sodium sulfonate (PolyNaSS) grafting onto the osseo-integration of a polyethylene terephthalate artificial ligament (Ligament Advanced Reinforcement System, LARS™) used for Anterior Cruciate Ligament (ACL). The performance of grafted and non-grafted ligaments was assessed in vitro by culturing human osteoblasts under osteogenic induction and this demonstrated that the surface modification was capable of up-regulating the secretion of ALP and induced higher level of mineralisation as measured 6 weeks post-seeding by Micro-Computed Tomography. Grafted and non-grafted LARS™ were subsequently implanted in an ovine model for ACL reconstruction and the ligament-to-bone interface was evaluated by histology and biomechanical testings 3 and 12 months post-implantation. The grafted ligaments exhibited more frequent direct ligament-to-bone contact and bone formation in the core of the ligament at the later time point than the non-grafted specimens, the grafting also significantly reduced the fibrous encapsulation of the ligament 12 months post-implantation. However, this improved osseo-integration was not translated into a significant increase in the biomechanical pull-out loads. These results provide evidences that PolyNaSS grafting improved the osseo-integration of the artificial ligament within the bone tunnels. This might positively influence the outcome of the surgical reconstructions, as higher ligament stability is believed to limit micro-movement and therefore permits earlier and enhanced healing.

Control, shift, insert : living and enacting policy in teachers' day/night work

This thesis investigates the experiences of teachers who trialled an electronic curriculum and assessment tool in the wider context of text-mediated ruling relations organising their work. Problematised as policy and text, this tool is interrogated as a 'solution' to problems perceived in teachers' work in an era of increased accountability. It provides evidence that teachers' work is shaped by forces operating outside their control and mediated by the policy discourses and subjectivities available to them.

An exploratory study to evaluate whether medical nutrition therapy can improve dietary intake in hospital patients who eat poorly

Background and aims The Australasian Nutrition Care Day Survey (ANCDS) reported two-in-five patients consume ≤50% of the offered food in Australian and New Zealand hospitals. After controlling for confounders (nutritional status, age, disease type and severity), the ANCDS also established an independent association between poor food intake and increased in-hospital mortality. This study aimed to evaluate if medical nutrition therapy (MNT) could improve dietary intake in hospital patients eating poorly. Methods An exploratory pilot study was conducted in the respiratory, neurology and orthopaedic wards of an Australian hospital. At baseline, percentage food intake (0%, 25%, 50%, 75%, and 100%) was evaluated for each main meal and snack for a 24-hour period in patients hospitalised for ≥2 days and not under dietetic review. Patients consuming ≤50% of offered meals due to nutrition-impact symptoms were referred to ward dietitians for MNT. Food intake was re-evaluated on the seventh day following recruitment (post-MNT). Results 184 patients were observed over four weeks; 32 patients were referred for MNT. Although baseline and post-MNT data for 20 participants (68±17years, 65% females) indicated a significant increase in median energy and protein intake post-MNT (3600kJ/day, 40g/day) versus baseline (2250kJ/day, 25g/day) (p<0.05), the increased intake met only 50% of dietary requirements. Persistent nutrition impact symptoms affected intake. Conclusion In this pilot study whilst dietary intake improved, it remained inadequate to meet participants’ estimated requirements due to ongoing nutrition-impact symptoms. Appropriate medical management and early enteral feeding could be a possible solution for such patients.

Work-based assessment : qualitative perspectives of novice nutrition and dietetics educators

Background The assessment of competence for health professionals including nutrition and dietetics professionals in work-based settings is challenging. The present study aimed to explore the experiences of educators involved in the assessment of nutrition and dietetics students in the practice setting and to identify barriers and enablers to effective assessment. Methods A qualitative research approach using in-depth interviews was employed with a convenience sample of inexperienced dietitian assessors. Interviews explored assessment practices and challenges. Data were analysed using a thematic approach within a phenomenological framework. Twelve relatively inexperienced practice educators were purposefully sampled to take part in the present study. Results Three themes emerged from these data. (i) Student learning and thus assessment is hindered by a number of barriers, including workload demands and case-mix. Some workplaces are challenged to provide appropriate learning opportunities and environment. Adequate support for placement educators from the university, managers and their peers and planning are enablers to effective assessment. (ii) The role of the assessor and their relationship with students impacts on competence assessment. (iii) There is a lack of clarity in the tasks and responsibilities of competency-based assessment. Conclusions The present study provides perspectives on barriers and enablers to effective assessment. It highlights the importance of reflective practice and feedback in assessment practices that are synonymous with evidence from other disciplines, which can be used to better support a work-based competency assessment of student performance.

From science to management : using Bayesian networks to learn about Lyngbya

Toxic blooms of Lyngbya majuscula occur in coastal areas worldwide and have major ecological, health and economic consequences. The exact causes and combinations of factors which lead to these blooms are not clearly understood. Lyngbya experts and stakeholders are a particularly diverse group, including ecologists, scientists, state and local government representatives, community organisations, catchment industry groups and local fishermen. An integrated Bayesian Network approach was developed to better understand and model this complex environmental problem, identify knowledge gaps, prioritise future research and evaluate management options.

Modeling the viability of the free-ranging cheetah population in Namibia : an object-oriented Bayesian network approach

Conservation of free-ranging cheetah (Acinonyx jubatus) populations is multi faceted and needs to be addressed from an ecological, biological and management perspective. There is a wealth of published research, each focusing on a particular aspect of cheetah conservation. Identifying the most important factors, making sense of various (and sometimes contrasting) findings, and taking decisions when little or no empirical data is available, are everyday challenges facing conservationists. Bayesian networks (BN) provide a statistical modeling framework that enables analysis and integration of information addressing different aspects of conservation. There has been an increased interest in the use of BNs to model conservation issues, however the development of more sophisticated BNs, utilizing object-oriented (OO) features, is still at the frontier of ecological research. We describe an integrated, parallel modeling process followed during a BN modeling workshop held in Namibia to combine expert knowledge and data about free-ranging cheetahs. The aim of the workshop was to obtain a more comprehensive view of the current viability of the free-ranging cheetah population in Namibia, and to predict the effect different scenarios may have on the future viability of this free-ranging cheetah population. Furthermore, a complementary aim was to identify influential parameters of the model to more effectively target those parameters having the greatest impact on population viability. The BN was developed by aggregating diverse perspectives from local and independent scientists, agents from the national ministry, conservation agency members and local fieldworkers. This integrated BN approach facilitates OO modeling in a multi-expert context which lends itself to a series of integrated, yet independent, subnetworks describing different scientific and management components. We created three subnetworks in parallel: a biological, ecological and human factors network, which were then combined to create a complete representation of free-ranging cheetah population viability. Such OOBNs have widespread relevance to the effective and targeted conservation management of vulnerable and endangered species.

Tangled Webs : Using Bayesian Networks in the Fight Against Infection

Bayesian networks (BNs) provide a statistical modelling framework which is ideally suited for modelling the many factors and components of complex problems such as healthcare-acquired infections. The methicillin-resistant Staphylococcus aureus (MRSA) organism is particularly troublesome since it is resistant to standard treatments for Staph infections. Overcrowding and understa�ng are believed to increase infection transmission rates and also to inhibit the effectiveness of disease control measures. Clearly the mechanisms behind MRSA transmission and containment are very complicated and control strategies may only be e�ective when used in combination. BNs are growing in popularity in general and in medical sciences in particular. A recent Current Content search of the number of published BN journal articles showed a fi�ve fold increase in general and a six fold increase in medical and veterinary science from 2000 to 2009. This chapter introduces the reader to Bayesian network (BN) modelling and an iterative modelling approach to build and test the BN created to investigate the possible role of high bed occupancy on transmission of MRSA while simultaneously taking into account other risk factors.

Beyond compliance : project on an integrated systems approach for pest risk management in South East Asia

The Beyond Compliance project, which began in July 2011 with funding from the Standards and Trade Development Facility for 2 years, aims to enhance competency and confidence in the South East Asian sub-region by applying a Systems Approach for pest risk management. The Systems Approach involves the use of integrated measures, at least two of which are independent, that cumulatively reduce the risk of introducing exotic pests through trade. Although useful in circumstances where single measures are inappropriate or unavailable, the Systems Approach is inherently more complicated than single-measure approaches, which may inhibit its uptake. The project methodology is to take prototype decision-support tools, such as Control Point-Bayesian Networks (CP-BN), developed in recent plant health initiatives in other regions, including the European PRATIQUE project, and to refine them within this sub-regional context. Case studies of high-priority potential agricultural trade will be conducted by National Plant Protection Organizations of participating South East Asian countries in trials of the tools, before further modifications. Longer term outcomes may include: more robust pest risk management in the region (for exports and imports); greater inclusion of stakeholders in development of pest risk management plans; increased confidence in trade negotiations; and new opportunities for trade.

A Bayesian network approach to modelling temporal behaviour of Lyngbya majuscula bloom initiation

Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide. Deception Bay, in Northern Moreton Bay, Queensland, has a history of Lyngbya blooms, and forms a case study for this investigation. The South East Queensland (SEQ) Healthy Waterways Partnership, collaboration between government, industry, research and the community, was formed to address issues affecting the health of the river catchments and waterways of South East Queensland. The Partnership coordinated the Lyngbya Research and Management Program (2005-2007) which culminated in a Coastal Algal Blooms (CAB) Action Plan for harmful and nuisance algal blooms, such as Lyngbya majuscula. This first phase of the project was predominantly of a scientific nature and also facilitated the collection of additional data to better understand Lyngbya blooms. The second phase of this project, SEQ Healthy Waterways Strategy 2007-2012, is now underway to implement the CAB Action Plan and as such is more management focussed. As part of the first phase of the project, a Science model for the initiation of a Lyngbya bloom was built using Bayesian Networks (BN). The structure of the Science Bayesian Network was built by the Lyngbya Science Working Group (LSWG) which was drawn from diverse disciplines. The BN was then quantified with annual data and expert knowledge. Scenario testing confirmed the expected temporal nature of bloom initiation and it was recommended that the next version of the BN be extended to take this into account. Elicitation for this BN thus occurred at three levels: design, quantification and verification. The first level involved construction of the conceptual model itself, definition of the nodes within the model and identification of sources of information to quantify the nodes. The second level included elicitation of expert opinion and representation of this information in a form suitable for inclusion in the BN. The third and final level concerned the specification of scenarios used to verify the model. The second phase of the project provides the opportunity to update the network with the newly collected detailed data obtained during the previous phase of the project. Specifically the temporal nature of Lyngbya blooms is of interest. Management efforts need to be directed to the most vulnerable periods to bloom initiation in the Bay. To model the temporal aspects of Lyngbya we are using Object Oriented Bayesian networks (OOBN) to create ‘time slices’ for each of the periods of interest during the summer. OOBNs provide a framework to simplify knowledge representation and facilitate reuse of nodes and network fragments. An OOBN is more hierarchical than a traditional BN with any sub-network able to contain other sub-networks. Connectivity between OOBNs is an important feature and allows information flow between the time slices. This study demonstrates more sophisticated use of expert information within Bayesian networks, which combine expert knowledge with data (categorized using expert-defined thresholds) within an expert-defined model structure. Based on the results from the verification process the experts are able to target areas requiring greater precision and those exhibiting temporal behaviour. The time slices incorporate the data for that time period for each of the temporal nodes (instead of using the annual data from the previous static Science BN) and include lag effects to allow the effect from one time slice to flow to the next time slice. We demonstrate a concurrent steady increase in the probability of initiation of a Lyngbya bloom and conclude that the inclusion of temporal aspects in the BN model is consistent with the perceptions of Lyngbya behaviour held by the stakeholders. This extended model provides a more accurate representation of the increased risk of algal blooms in the summer months and show that the opinions elicited to inform a static BN can be readily extended to a dynamic OOBN, providing more comprehensive information for decision makers.

What is an expert? A systems perspective on expertise

1. Expert knowledge continues to gain recognition as a valuable source of information in a wide range of research applications. Despite recent advances in defining expert knowledge, comparatively little attention has been given to how to view expertise as a system of interacting contributory factors, and thereby, to quantify an individual’s expertise. 2. We present a systems approach to describing expertise that accounts for many contributing factors and their interrelationships, and allows quantification of an individual’s expertise. A Bayesian network (BN) was chosen for this purpose. For the purpose of illustration, we focused on taxonomic expertise. The model structure was developed in consultation with professional taxonomists. The relative importance of the factors within the network were determined by a second set of senior taxonomists. This second set of experts (i.e. supra-experts) also provided validation of the model structure. Model performance was then assessed by applying the model to hypothetical career states in the discipline of taxonomy. Hypothetical career states were used to incorporate the greatest possible differences in career states and provide an opportunity to test the model against known inputs. 3. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher order nodes, “Quality of work” and “Total productivity”, had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite differences in the node weights provided by our supra-experts, there was remarkably good agreement among assessments of our hypothetical experts that accurately reflected differences we had built into them. 4. This systems approach provides a novel way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

Talk in activity during young children’s use of digital technologies at home

Internet-connected tablets and smart phones are being used increasingly by young children. Little is known, however, about their social interactions with family members when engaged with these technologies. This article examines video-recorded interactions between a father and his two young children, one aged 18 months using an iPhone and one aged three years accessing an iPad. Drawing on Ethnomethodology and Conversation Analysis, analysis establishes ways the family members engage and disengage in talk so as to manage their individual activity with mobile devices and accomplish interaction with each other. Findings are relevant for understanding children’s everyday practices with mobile technologies.

Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study : assessing the viability and sustainability of the dairy industry

Sustainability is a key driver for decisions in the management and future development of industries. The World Commission on Environment and Development (WCED, 1987) outlined imperatives which need to be met for environmental, economic and social sustainability. Development of strategies for measuring and improving sustainability in and across these domains, however, has been hindered by intense debate between advocates for one approach fearing that efforts by those who advocate for another could have unintended adverse impacts. Studies attempting to compare the sustainability performance of countries and industries have also found ratings of performance quite variable depending on the sustainability indices used. Quantifying and comparing the sustainability of industries across the triple bottom line of economy, environment and social impact continues to be problematic. Using the Australian dairy industry as a case study, a Sustainability Scorecard, developed as a Bayesian network model, is proposed as an adaptable tool to enable informed assessment, dialogue and negotiation of strategies at a global level as well as being suitable for developing local solutions.