Selecting social work students:lessons from research in Scotland

The issue of selection of students to social work programmes is one that remains highly contested. While it is clear that there is no single way of choosing the next generation of social work students, nevertheless, there are a number of strongly held beliefs about what ‘best practice’ means in this fraught field. These can be difficult to challenge, and even harder to shift, in spite of contrary evidence. This paper presents research conducted in Scotland in 2016 as part of the Scottish Government-sponsored Review of Social Work Education. The research set out to consider what selection processes were being used in Scotland and why; more fundamentally, it sought to explore the views of those involved in social work education alongside evidence about the outcomes of the selection processes (that is, data on student retention and success). The article concludes that while there is little evidence that one method of selection to social work programmes is intrinsically better than another, issues of fairness and transparency in selection, as well as diversity, remain pressing

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic