Recent Australian government targets for higher education participation have produced a flurry of activity focused on raising the aspirations of students from low socioeconomic status (SES) backgrounds. In this paper we test two key assumptions underpinning much of this activity: that students from low-SES backgrounds hold lower career aspirations; and that outreach activities appropriately target secondary school students, given that younger students' aspirations are relatively under-developed. Drawing on a sample of 3,504 students, we map the intersection of the career aspirations of students in Years 4, 6, 8, and 10 with SES and other demographic variables in order to contribute to the evidence base for academic, educational, and political work on access to higher education and the policies, practices, and outcomes that might ensue. Aspirations are assessed in terms of occupational certainty, occupational choice, occupational prestige, and occupational justification. We found fewer differences by year level and by SES than expected. Our analyses demonstrate both the complexity of students' career aspirations and some of the challenges associated with undertaking this kind of research, thus signalling the need for caution in the development of policy and interventions in this field.

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Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system.

Demand for higher education in Australia has doubled since 1989, increasing the number of students from diverse social, economic and academic backgrounds. Equity targets have seen a proliferation of programs and interventions aimed at encouraging school students, particularly those from low socio-economic status backgrounds, to participate in higher education. However, little is known about the specific occupational interests of school students upon which targeted strategies might effectively be designed and implemented. This paper examines school students' aspirations for specific careers that require a university education, in relation to student background and school-related variables. The analysis draws from a study of 6492 students from Years 3 to 12 in 64 New South Wales public schools. We found a complex array of factors relating to interest in different careers. Year level at school, gender and prior achievement were stronger predictors across many careers than factors such as SES, Indigenous status and school location. We argue that rather than taking a one-sizefits-all approach to encouraging participation in higher education, outreach activities should be targeted to take account of student diversity and inequalities that foster differing aspirations. ARTICLE HISTORY