Applying artificial neural network models to clinical decision making

Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed.     

The recruitment process for school-leavers: Practical and theoretical implications

Recruitment is viewed as a process whereby 16-year-old school-leavers and recruiters first come together through the mutual use of recruitment methods. In 1983-84, a school-leavers survey, recruiters survey and some Youth Training Scheme (YTS) case-studies were carried out in Doncaster and in Reading. For jobs, college courses and YTS entry, school-leavers found the Careers Service and directly contacting the recruiter to be the most consistently 'effective' recruitment methods. Employers and colleges relied mostly on direct approach by school-leavers. Changes to recruitment brought about by YTS are considered. Implications of the findings for careers guidance practice and for theories of occupational choice and entry are explored. An alternative perspective to the sequential concept of occupational choice and entry is put forward.