Validity and reliability of the DMSES UK : a measure of self-efficacy for type 2 diabetes self-management

Objectives Self-efficacy is an important outcome measure of self-management interventions. We aimed to establish UK validity and reliability of the diabetes management self-efficacy scale (DMSES). Methods The 20 item DMSES was available for Dutch and US populations. Consultation with people with type 2 diabetes and health professionals established UK content and face validity resulting in item reduction to 15. Participants were adults with type 2 diabetes enrolled in a randomised controlled trial (RCT) of the diabetes manual, a self-management education intervention, with an HbA1c over 7% and who understood English. Baseline trial data and follow-up control group data were used. Results A total of 175 participants completed all 15 items. Pearson’s correlation coefficient of −0.46 (P 0.30. Cronbach’s alpha was 0.89 over all items. Conclusion This evaluation demonstrates that the scale has good internal reliability, internal consistency, construct validity, criterion validity, and test-retest reliability. Practice Implications The 15 item DMSES UK is suitable for use in research and clinical settings to measure the self-efficacy of people living with type 2 diabetes in managing their diabetes

Effects of the diabetes manual 1:1 structured education in primary care

Aims  To determine the effects of the Diabetes Manual on glycaemic control, diabetes-related distress and confidence to self-care of patients with Type 2 diabetes. Methods  A cluster randomized, controlled trial of an intervention group vs. a 6-month delayed-intervention control group with a nested qualitative study. Participants were 48 urban general practices in the West Midlands, UK, with high population deprivation levels and 245 adults with Type 2 diabetes with a mean age of 62 years recruited pre-randomization. The Diabetes Manual is 1:1 structured education designed for delivery by practice nurses. Measured outcomes were HbA1c, cardiovascular risk factors, diabetes-related distress measured by the Problem Areas in Diabetes Scale and confidence to self-care measured by the Diabetes Management Self-Efficacy Scale. Outcomes were assessed at baseline and 26 weeks. Results  There was no significant difference in HbA1c between the intervention group and the control group [difference −0.08%, 95% confidence interval (CI) −0.28, 0.11]. Diabetes-related distress scores were lower in the intervention group compared with the control group (difference −4.5, 95% CI −8.1, −1.0). Confidence to self-care Scores were 11.2 points higher (95% CI 4.4, 18.0) in the intervention group compared with the control group. The patient response rate was 18.5%. Conclusions  In this population, the Diabetes Manual achieved a small improvement in patient diabetes-related distress and confidence to self-care over 26 weeks, without a change in glycaemic control. Further study is needed to optimize the intervention and characterize those for whom it is more clinically and psychologically effective to support its use in primary care

LABOR AND TRAINING NEEDS OF RURAL AMERICA

Labor and Human Capital,

Stochastic Scheduling of Wind-Integrated Power Systems

The cost of balancing supply and demand will increase as power systems are decarbonised, because the requirement for operating reserve will increase with the wind penetration, while the flexible fossil-fuel generators, which have been the traditional providers of reserve, will be displaced. While these costs can be mitigated through increased interconnection, energy storage, and demand-side market participation, a fundamental review of system operational policy is also needed to ensure that the available reserves are scheduled optimally. Stochastic Unit Commitment can find the commitment and dispatch decisions that minimise the expected system costs, including the potential costs of unserved energy, given the short-term uncertainties of wind and other variables. It therefore has the potential to provide the most efficient possible paradigm for the operation of wind-integrated systems. Because the system’s ability to respond to wind fluctuations is constrained by intertemporal limitations of the other components, time domain simulations are needed to assess the performance of different operational strategies or generator fleet characteristics. However, Stochastic Unit Commitment has demanding computational requirements that can render it impractical for long-term simulations of a large power system. This thesis develops a new tool for simulating the operation of large, wind-integrated power systems using stochastic scheduling, with the emphasis on computational efficiency. Embedded within it are new models for characterising time series of aggregated wind output and wind forecast errors; these models are integrated with a Stochastic Unit Commitment algorithm within a Monte Carlo framework. We explore simplifications that can mitigate the computational burden without unduly compromising the quality of the analysis. Simulations with the tool show that fully stochastic scheduling can reduce operating costs by around 4% relative to traditional deterministic approaches, in a system with a 50% wind penetration

A Labelled Analytic Theorem Proving Environment for Categorial Grammar

We present a system for the investigation of computational properties of categorial grammar parsing based on a labelled analytic tableaux theorem prover. This proof method allows us to take a modular approach, in which the basic grammar can be kept constant, while a range of categorial calculi can be captured by assigning different properties to the labelling algebra. The theorem proving strategy is particularly well suited to the treatment of categorial grammar, because it allows us to distribute the computational cost between the algorithm which deals with the grammatical types and the algebraic checker which constrains the derivation.Comment: 11 pages, LaTeX2e, uses examples.sty and a4wide.st

A nonparametric algorithm for optimal stopping based on robust optimization

Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. We introduce a new approach for solving computationally-demanding stochastic optimal stopping problems with known probability distributions. The approach uses simulation to construct a robust optimization problem that approximates the stochastic optimal stopping problem to any arbitrary accuracy; we then solve the robust optimization problem to obtain near-optimal Markovian stopping rules for the stochastic optimal stopping problem. In this paper, we focus on designing algorithms for solving the robust optimization problems that approximate the stochastic optimal stopping problems. These robust optimization problems are challenging to solve because they require optimizing over the infinite-dimensional space of all Markovian stopping rules. We overcome this challenge by characterizing the structure of optimal Markovian stopping rules for the robust optimization problems. In particular, we show that optimal Markovian stopping rules for the robust optimization problems have a structure that is surprisingly simple and finite-dimensional. We leverage this structure to develop an exact reformulation of the robust optimization problem as a zero-one bilinear program over totally unimodular constraints. We show that the bilinear program can be solved in polynomial time in special cases, establish computational complexity results for general cases, and develop polynomial-time heuristics by relating the bilinear program to the maximal closure problem from graph theory. Numerical experiments demonstrate that our algorithms for solving the robust optimization problems are practical and can outperform state-of-the-art simulation-based algorithms in the context of widely-studied stochastic optimal stopping problems from high-dimensional option pricing