'One student might get one opportunity and then the next student won't get anything like that': Inequities in Australian career education and recommendations for a fairer future.

Access to quality career advice is important for economic, personal and equity reasons, yet, in many countries around the world, career-education provision is of varying quality and quantity within school settings. Given the inconsistencies in career-education resourcing and provision, what is not clearly understood is how students from low socioeconomic status (low SES) backgrounds experience career-education provision and the extent to which it shapes their post-school futures. Drawing on Australian research, this paper explores the career-education experiences of high-school students from low SES backgrounds. Bourdieu's tools of field, habitus and capital are used as a theoretical framework to understand how career education can influence students' imagining and achieving their career goals. The findings reported in this paper contribute nuanced understandings of career education to students from low SES backgrounds and recommends how all students can benefit from an embedded approach to career education in schools.

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.