Creating an effective prevention curriculum to reduce transport risks and injuries

Adolescent injury is a significant health concern and can be a result of the adolescents engagement in transport-related behaviours. There is however significant planning and formative research needed to inform prevention programme design. This presentation reports on the development and evaluation of a curriculum programme that was shown to be effective in reducing transport-related risks and injuries. Early adolescents report injuries resulting from a number of transport-related behaviours including those associated with riding a bicycle, a motorcycle, and as a passenger (survey of 209 Year 9 students). In focus groups, students (n=30) were able to describe the context of transport risks and injuries. Such information provided evidence of the need for an intervention and ecologically valid data on which to base programme design including insights into the language, culture and development of adolescents and their experiences with transport risks. Additional information about teaching practices and implementation issues were explored in interviews with 13 teachers. A psychological theory was selected to operationalise the design of the programmes that drew on such preparatory data. The programme, Skills for Preventing Injury in Youth was evaluated with 197 participating and 137 control students (13–14 year olds). Results showed a significant difference between the intervention and control groups from baseline to 6-month follow-up in a number of transport-related risk behaviours and transport-related injuries. The programme thus demonstrated potential in reduce early adolescents transport risk behaviours and associated harm. Discussion will involve the implications of the development research process in designing road safety interventions.

XML documents clustering using tensor space model - A preliminary study

A hierarchical structure is used to represent the content of the semi-structured documents such as XML and XHTML. The traditional Vector Space Model (VSM) is not sufficient to represent both the structure and the content of such web documents. Hence in this paper, we introduce a novel method of representing the XML documents in Tensor Space Model (TSM) and then utilize it for clustering. Empirical analysis shows that the proposed method is scalable for a real-life dataset as well as the factorized matrices produced from the proposed method helps to improve the quality of clusters due to the enriched document representation with both the structure and the content information.