Family-focused weight management program for five- to nine-year-olds incorporating parenting skills training with healthy lifestyle information to support behaviour modification

his case study aims to describe how general parenting principles can be used as part of parent-led, family-focused child weight management that is in line with current Australian Clinical Practice Guidelines. A parent-led, family-focused child weight management program was designed for use by dietitians with parents of young children (five- to nine-year-olds). The program utilises the cornerstones of overweight treatment: diet, activity, behaviour modification and family support delivered in an age-appropriate, family-focused manner. Parents participate in 16 sessions (4 parenting-focused, 8 lifestyle-focused and 4 individual telephone support calls) conducted weekly, fortnightly then monthly over six months. This case study illustrates how a family used the program, resulting in reduced degree of overweight and stabilised waist circumference in the child over 12 months. In conclusion, linking parenting skills to healthy family lifestyle education provides an innovative approach to family-focused child weight management. It addresses key Australian Clinical Practice Guidelines, works at the family level, and provides a means for dietitians to easily adopt age-appropriate behaviour modification as part of their practice.

Providing technical support for a university radiological laboratory

Queensland University of Technology has a long standing in providing tertiary education and training in ionising radiation. The radiological laboratory plays an important part in this education and training. As radiological applications are diversified in the fields of health and environment, the laboratory provides support for a number of scenarios in the use of experimental situations in radiation detection and radiation protection. This paper discusses the role that a radiological laboratory technician plays in the functionality of a radiological laboratory.

Sustainable infrastructure project planning : progress in contemporary decision support tools

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described.

Thinking spatially, acting collaboratively : a GIS-based health decision support system for improving the collaborative health-planning practice

The field of collaborative health planning faces significant challenges due to the lack of effective information, systems and the absence of a framework to make informed decisions. These challenges have been magnified by the rise of the healthy cities movement, consequently, there have been more frequent calls for localised, collaborative and evidence-driven decision-making. Some studies in the past have reported that the use of decision support systems (DSS) for planning healthy cities may lead to: increase collaboration between stakeholders and the general public, improve the accuracy and quality of the decision-making processes and improve the availability of data and information for health decision-makers. These links have not yet been fully tested and only a handful of studies have evaluated the impact of DSS on stakeholders, policy-makers and health planners. This study suggests a framework for developing healthy cities and introduces an online Geographic Information Systems (GIS)-based DSS for improving the collaborative health planning. It also presents preliminary findings of an ongoing case study conducted in the Logan-Beaudesert region of Queensland, Australia. These findings highlight the perceptions of decision-making prior to the implementation of the DSS intervention. Further, the findings help us to understand the potential role of the DSS to improve collaborative health planning practice.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.