Independence or Protection - does it have to be a choice? Reflections on the abuse people with learning disabilities in Cornwall

This commentary will use recent events in Cornwall to highlight the ongoing abuse of adults with learning disabilities in England. It will critically explore how two parallel policy agendas – namely, the promotion of choice and independence for adults with learning disabilities and the development of adult protection policies – have failed to connect, thus allowing abuse to continue to flourish. It will be argued that the abuse of people with learning disabilities can only be minimised by policies which reflect an understanding that choice and independence must necessarily be mediated by effective adult protection measures. Such protection needs to include not only an appropriate regulatory framework, access to justice and well-qualified staff, but also a more critical and reflective approach to the current orthodoxy which promotes choice and independence as the only acceptable goals for any person with a learning disability.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.