Targeting Mr Average: Participation, gender equity and school sport partnerships

The School Sport Partnership Programme (SSPP) is one strand of the national strategy for physical education and school sport in England, the physical education and school sport Club Links Strategy (PESSCL). The SSPP aims to make links between school physical education (PE) and out of school sports participation, and has a particular remit to raise the participation levels of several identified under-represented groups, of which girls and young women are one. National evaluations of the SSPP show that it is beginning to have positive impacts on young people's activity levels by increasing the range and provision of extra curricular activities (Office for Standards in Education (OFSTED), 2003, 2004, 2005; Loughborough Partnership, 2005, 2006). This paper contributes to the developing picture of the phased implementation of the programme by providing qualitative insights into the work of one school sport partnership with a particular focus on gender equity. The paper explores the ways in which gender equity issues have been explicitly addressed within the 'official texts' of the SSPP; how these have shifted over time and how teachers are responding to and making sense of these in their daily practice. Using participation observation, interview and questionnaire data, the paper explores how the coordinators are addressing the challenge of increasing the participation of girls and young women. The paper draws on Walby's (2000) conceptualisation of different kinds of feminist praxis to highlight the limitations of the coordinators' work. Two key themes from the data and their implications are addressed: the dominance of competitive sport practices and the PE professionals' views of targeting as a strategy for increasing the participation of under-represented groups. The paper concludes that coordinators work within an equality or difference discourse with little evidence of the transformative praxis needed for the programme to be truly inclusive. © 2008 Taylor & Francis

Accuracy Analysis of an Image Guided Robotic Urology Surgery System

We present an evaluation of the accuracy of a system for image guided radical prostatectomy using the daVinci telemanipulator. The system is split into components and ten sources of error identified. The magnitude of three of these error sources; segmentation of bone from MRI, registration to patient using intraoperative ultrasound, and endoscope tracking error is determined experimentally. The remaining errors are estimated from the literature. We demonstrate that the distribution of ultrasound slices used for registration can reduce the system error by up to 0.7mm. Our results show that our system can localise the prostate to within 3.7mm RMS, and that the largest component of the this error is the segmentation of the pelvic bone from MRI

Design and Evaluation of Image Guidance Systems for RARP

INTRODUCTION: There is a strong appetite amongst laparoscopic surgeons for image guidance during the procedure. It seems intuitively obvious that providing the surgeon with additional information on the location of unseen anatomy can only improve patient outcomes. This is not necessarily the case however. If the system gives information that is not relevant to the procedure it becomes a distraction. Similarly, if the system has large alignment errors the information may be dangerously wrong. One danger is that image guidance systems can be developed on an ad-hoc basis based not on targeted clinical goals but on the technical expertise and research goals of the scientists and engineers involved. Such a system may or may not benefit the patient. However, there is a real danger, as discussed by [1], that such systems will be introduced into surgical practice without proper assessment. We present our minimalist image guidance system for robot assisted radical prostatectomy together with a design and evaluation framework built upwards from the desired clinical outcomes

Image Guided Robotic Radical Prostatectomy

We present results of early trials of a system to overlay preoperative MRI onto endoscope video taken from a daVinci endoscope system during robotic radical prostatectomy. The endoscope is calibrated and tracked so that the MRI can be overlaid in the same coordinate system as the video data and projected onto the screen with the correct camera parameters. The system has two potential applications. The first is that it enables the surgeon to easily refer to the preoperative MRI during surgery. Secondly, it may serve as an initialisation for a model to video registration method to enable the preoperative data to be updated during the procedure. The system has been trialled in five patients, with overlay images provided to the surgeon during surgery in two cases. Further trials are ongoing

An inside story: tracking experiences, challenges and successes in a joint specialist performing arts college

In England the government’s specialist schools initiative is transforming the nature of secondary education. A three-year longitudinal case study tracked the effects of specialist performing arts college status on two schools. The sites were a mainstream school drawing pupils from an area of high social deprivation and disadvantage, and a special school catering for pupils with profound and \ud multiple learning difficulties, which were awarded joint performing arts college status. The government’s \ud preferred criterion for judging the success of specialist schools is improvement in whole-school examination results. The authors argue that this is a crude and inappropriate measure for these case study schools and probably others. Using questionnaires, interviews and documentation they tell an ‘inside story’ of experiences, challenges and achievements, from the perspectives of the schools’ mangers, staff and pupils. Alternative ‘value-added’ features emerged that were positive indicators of enrichment and success in both schools

Stochastic learning in a neural network with adapting synapses

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.Comment: 25 pages, LaTeX fil

Use of a CT statistical deformation model for multi-modal pelvic bone segmentation

We present a segmentation algorithm using a statistical deformation model constructed from CT data of adult male pelves coupled to MRI appearance data. The algorithm allows the semi-automatic segmentation of bone for a limited population of MRI data sets. Our application is pelvic bone delineation from pre-operative MRI for image guided pelvic surgery. Specifically, we are developing image guidance for prostatectomies using the daVinci telemanipulator. Hence the use of male pelves only. The algorithm takes advantage of the high contrast of bone in CT data, allowing a robust shape model to be constructed relatively easily. This shape model can then be applied to a population of MRI data sets using a single data set that contains both CT and MRI data. The model is constructed automatically using fluid based non-rigid registration between a set of CT training images, followed by principal component analysis. MRI appearance data is imported using CT and MRI data from the same patient. Registration optimisation is performed using differential evolution. Based on our limited validation to date, the algorithm may outperform segmentation using non-rigid registration between MRI images without the use of shape data. The mean surface registration error achieved was 1.74 mm. The algorithm shows promise for use in segmentation of pelvic bone from MRI, though further refinement and validation is required. We envisage that the algorithm presented could be extended to allow the rapid creation of application specific models in various imaging modalities using a shape model based on CT data