The Rural Clinical Division: a student journey to rural medicine

Background: The University of Queensland has through an Australian Government initiative, established a Rural Clinical Division (RCD) at four regional sites in the southern and central Queensland. Over the fi rst four years of the existence of the RCD, an integrated package of innovative medical education has been developed. Method: The integrated aspects of the RCD program include: The Rural Medical Rotation: Every medical student undertakes an eight week rural rotation in Year 3. Year 3 and 4 MBBS - 100 students are currently spending one to two years in the rural school and demand is increasing. Interprofessional Education - Medical and Allied Health students attend lectures, seminars and workshops together and often share the same rural clinical placement. Rural health projects - allow students to undertake a project of benefi t to the rural community. Information Technology (IT) - the Clinical Discussion Board (CDB) and Personal Digital Assistants (PDA) demonstrate the importance of IT to medical students in the 21st century. Changing the Model of Medical Education - The Leichhardt Community Attachment Placement (LCAP), is a pilot study that resulted in the addition of three interns to the rural workforce. All aspects of the RCD are evaluated with surveys using both qualitative and quantitative free response questions, completed by all students regularly throughout the academic year. Results: Measures of impact include: Student satisfaction and quality of teaching surveys – 86-91% of students improved their clinical skills and understanding across all rotations. Academic results and progress – RCD students out-perform their urban colleagues. Intent to work in rural areas – 90% of students reported a greater interest in rural medicine. Intern numbers – rural / regional intern placements are increasing. Conclusions: The RCD proves to be a site for innovations all designed to help reach our primary goal of fostering increased recruitment of a rural medical workforce.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.