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Rural Australian patients continue to receive inadequate medical attention. One potential solution to this is to train Indigenous Australians to become medical doctors and return to their community to serve their people. The study aims to examine whether Indigenous medical students have a stronger intention to practice in underserved communities.

A sample of Indigenous (N = 17) and non-Indigenous students (N = 188) from a medical program in Sydney was surveyed about their medical self-concept and motivation. Confirmatory factor analysis (CFA) was conducted, group differences were tested, and correlation patterns were examined.

CFA found seven distinct factors – three medical self-concepts (affective, cognitive, and cultural competence), one motivation factor, and three work-related variables – intention to serve underserved communities (intention), understanding of Indigenous health (understanding), and work-related anxiety (anxiety). Indigenous medical students were higher in cultural competence, intention, and understanding. Both the affective and cognitive components of medical self-concept were more highly correlated with intention and understanding for Indigenous students than for non-Indigenous students.

It is important to examine medical students’ self-concepts as well as their cultural characteristics and strengths that seed success in promoting service to underserved Indigenous communities.

The findings show that Indigenous medical students tended to understand Indigenous health issues better and to be more willing to serve underserved Indigenous communities. By enhancing both the affective and cognitive components of medical self-concepts, the “home-grown” medical education program is more likely to produce medical doctors to serve underserved communities with a good understanding of Indigenous health.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.

Our children may learn about the heroes of the past.

Our task is to make ourselves architects of the future.

The twenty-first century confronts society with challenges that will determine the future of humanity and the planet. Such challenges defy traditional analysis. Paralyzed by the inadequacy of our standard logic, on which much of traditional scholarship relies, we search for meaningful and effective understandings that can guide us – understandings that seem inherently wise and just, and not simply empirically confirmable. Few of us question the need for wisdom, yet to date, academic scholarship has failed to address the role that it plays, and could play, in supporting international organizational processes capable of addressing the world’s most demanding societal challenges.² This chapter explores the nature of pragmatic wisdom – wisdom that incorporates both profound understanding and action. It uses the founding of an international development initiative, Uniterra, to highlight the need for and influence of wisdom in international organizational processes and outcomes. Uniterra’s core structure and central process involve partnering – forming networks of non-hierarchical relationships. The chapter therefore investigates the wisdom needed to create and maintain global partnerships. Given the chapter’s focus on pragmatic wisdom, it also explores the concepts of hope and courage, for without hope and courage, wisdom could never move beyond conceptualization to action. The writing style purposely differs from that of most scholarly articles. Beyond presenting a specific case, the writing offers readers the opportunity to experience wisdom via indigenous proverbs from a wide range of the world’s more pragmatic wisdom traditions. So as not to interrupt readers’ appreciation of the proverbs or reduce their impact or meaning merely to the underlying logical constructs, the chapter uses endnotes rather than more traditional text references.

The 21st century confronts society with challenges that will determine the future of humanity and the planet. Such challenges defy traditional analysis. Paralyzed by the inadequacy of our standard logic, on which much of traditional scholarship relies, we search for meaningful and effective understandings that can guide us—understandings that seem inherently wise and just, and not simply empirically confirmable. Few of us question the need for wisdom, yet to date, academic scholarship has failed to address the role that it plays, and could play, in supporting international organizational processes capable of addressing the world’s most demanding societal challenges. ² This chapter explores the nature of pragmatic wisdom—wisdom that incorporates both profound understanding and action. It uses the founding of an international development initiative, Uniterra, to highlight the need for and influence of wisdom in international organizational processes and outcomes. Uniterra’s core structure and central process involve partnering—forming networks of nonhierarchical relationships. The chapter therefore investigates the wisdom needed to create and maintain global partnerships. Given the chapter’s focus on pragmatic wisdom, it also explores the concepts of hope and courage, for without hope and courage, wisdom could never move beyond conceptualization to action. The writing style purposely differs from that of most scholarly articles. Beyond presenting a specific case, the writing offers readers the opportunity to experience wisdom via indigenous proverbs from a wide range of the world’s more pragmatic wisdom traditions. So as not to interrupt readers’ appreciation of the proverbs or reduce their impact or meaning merely to the underlying logical constructs, the chapter uses endnotes rather than more traditional text references.

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled “Applications of Artificial Intelligence in Economics and Finance” at the “2003 International Conference on Artificial Intelligence” (IC-AI ’03) held at the Monte Carlo Resort, Las Vegas, NV, USA, June 23–26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations.