Lincolnshire exercise referral evaluation

This document reports on evaluation work completed by the University of Lincoln through the School of Sport and Exercise Science. It examines data stored on the Lincolnshire Sports Partnership’s parachute system regarding patients attending Lincolnshire’s Exercise Referral (ER) Programme, a service funded by Public Health. The analysis was in response to specific questions determined by exercise practitioners, the Lincolnshire Sports Partnership and Public Health Lincolnshire. Data was analysed via a number of statistical methods including Chi-squared and Logistic Regression. The data spanned a period of 3.5 years and included all patients in the database starting a 12-week ER programme between 10th March 2009 through to 22nd August 2012. There were 6637 eligible patients, of which 62.3% completed a 12-week ER programme. Headline findings from the evaluative research identified; 1) There was a significant relationship between those patients who completed the referral programme and a reduction in body mass index (BMI); 2) Those patients completing nine or more (out of 12) weeks of the referral programme were significantly more likely to complete. The number of sessions within a week did not influence completion; 3) There was a significantly increased likelihood for those patients who pay for exercise referral to complete the programme. This was regardless of the deprivation score of their home postcode and 4) There was no significant relationship between the way a referral is initiated and a patient completing a referral programme. More than half of these data were missing; however, hence the validity of this finding is impaired. These findings were used to generate recommendations regarding the data that is currently collected via the parachute system and the processes that are employed by the ER programmes

FDI and the Availability of Dublin Office Space. ESRI Research Notes 2015/3/2

Foreign direct investment (FDI) is an important component of industrial policy in Ireland. Having pursued this policy for many years, Ireland is one of the most FDI-intensive economies in the OECD. The factors underpinning Ireland’s success in attracting FDI have been well documented and include EU membership, native English-speaking, low corporate tax rate, young and skilled labour force and demonstration effects.2 A recent policy statement on FDI identifies the role of cities as becoming increasingly important in FDI flows and cites the attractiveness of Dublin as a key determinant in Ireland’s overall FDI performance (Department of Jobs, Enterprise and Innovation, 2014)

European precipitation connections with large-scale mean sea-level pressure (MSLP) fields

To advance understanding of hydroclimatological processes, this paper links spatiotemporal variability in gridded European precipitation and large-scale mean sea-level pressure (MSLP) time series (1957–2002) using monthly concurrent correlation. Strong negative (positive) correlation near Iceland and (the Azores) is apparent for precipitation in northwest Europe, confirming a positive North Atlantic Oscillation (NAO) association. An opposing pattern is found for southwest Europe, and the Mediterranean in winter. In the lee of mountains, MSLP correlation is lower reflecting reduced influence of westerlies on precipitation generation. Importantly, European precipitation is shown to be controlled by physically interpretable climate patterns that change in extent and position from month to month. In spring, MSLP–precipitation correlation patterns move and shrink, reaching a minimum in summer, before expanding in the autumn, and forming an NAO-like dipole in winter. These space–time shifts in correlation regions explain why fixed-point NAO indices have limited ability to resolve precipitation for some European locations and seasons

Bayesian nonparametric multivariate convex regression

In many applications, such as economics, operations research and reinforcement learning, one often needs to estimate a multivariate regression function f subject to a convexity constraint. For example, in sequential decision processes the value of a state under optimal subsequent decisions may be known to be convex or concave. We propose a new Bayesian nonparametric multivariate approach based on characterizing the unknown regression function as the max of a random collection of unknown hyperplanes. This specification induces a prior with large support in a Kullback-Leibler sense on the space of convex functions, while also leading to strong posterior consistency. Although we assume that f is defined over R^p, we show that this model has a convergence rate of log(n)^{-1} n^{-1/(d+2)} under the empirical L2 norm when f actually maps a d dimensional linear subspace to R. We design an efficient reversible jump MCMC algorithm for posterior computation and demonstrate the methods through application to value function approximation

Developing and modelling complex social interventions: introducing the Connecting People Intervention

Objectives: Modeling the processes involved in complex social interventions is important in social work practice, as it facilitates their implementation and translation into different contexts. This article reports the process of developing and modeling the connecting people intervention (CPI), a model of practice that supports people with mental health problems to enhance their social networks. Method: The CPI model was developed through an iterative process of focus group discussions with practitioners and service users and a two-stage Delphi consultation with relevant experts. Results: We discuss the intervention model and the processes it articulates to provide an example of the benefits of intervention modeling. Conclusions: Intervention modeling provides a visual representation of the process and outcomes of an intervention, which can assist practice development and lead to improved outcomes for service users