Reconfiguring or reproducing intra-professional boundaries? Specialist expertise, generalist knowledge and the ‘modernization’ of the medical workforce

Efforts to ‘modernize’ the clinical workforce of the English National Health Service have sought to reconfigure the responsibilities of professional groups in pursuit of more effective, joined-up service provision. Such efforts have met resistance from professions eager to protect their jurisdictions, deploying legitimacy claims familiar from the insights of the sociology of professions. Yet to date few studies of professional boundaries have grounded these insights in the specific context of policy challenges to the inter- and intra-professional division of labour, in relation the medical profession and other health-related occupations. In this paper we address this gap by considering the experience of newly instituted general practitioners (family physicians) with a special interest (GPSIs) in genetics, introduced to improve genetics knowledge and practice in primary care. Using qualitative data from four comparative case studies, we discuss how an established intra-professional division of labour within medicine—between clinical geneticists and GPs—was opened, negotiated and reclosed in these sites. We discuss the contrasting attitudes towards the nature of genetics knowledge and its application of GPSIs and geneticists, and how these were used to advance conflicting visions of what the nascent GPSI role should involve. In particular, we show how the claims to knowledge of geneticists and GPSIs interacted with wider policy pressures to produce a rather more conservative redistribution of power and responsibility across the intra-professional boundary than the rhetoric of modernization might suggest.

STORM - a novel information fusion and cluster interpretation technique

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.