Are we teaching our students what they need to know about ageing? Results from the National Survey of Undergraduate Teaching in Ageing and Geriatric Medicine

Introduction - Learning about ageing and the appropriate management of older patients is important for all doctors. This survey set out to evaluate what medical undergraduates in the UK are taught about ageing and geriatric medicine and how this teaching is delivered. Methods – An electronic questionnaire was developed and sent to the 28/31 UK medical schools which agreed to participate. Results – Full responses were received from 17 schools. 8/21 learning objectives were recorded as taught, and none were examined, across every school surveyed. Elder abuse and terminology and classification of health were taught in only 8/17 and 2/17 schools respectively. Pressure ulcers were taught about in 14/17 schools but taught formally in only 7 of these and examined in only 9. With regard to bio- and socio- gerontology, only 9/17 schools reported teaching in social ageing, 7/17 in cellular ageing and 9/17 in the physiology of ageing. Discussion – Even allowing for the suboptimal response rate, this study presents significant cause for concern with UK undergraduate education related to ageing. The failure to teach comprehensively on elder abuse and pressure sores, in particular, may be significantly to the detriment of older patients.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.