Enabling integrated knowledge acquisition and management in health care teams

Basing treatment, policy and planning decisions on the best available research knowledge remains a central principle in modern health care around the world, yet many health professionals find acquiring and managing published research knowledge challenging. In this paper, we report on a Soft Systems Methodology-based collaborative action research initiative with a specialist mental health service from the United Kingdom's National Health Service. Our objective was to design and implement improvements to their knowledge acquisition and management activities in order to facilitate sustained and effective evidence-based practices. We report on both the factors found to impede effective research knowledge acquisition and management and the development of more integrated knowledge management processes designed to improve the situation.     

Introduction to Sample Size Choice for Confidence Intervals Based on t Statistics

Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis System (SAS) proc power procedure to determine an appropriate sample size and achieve sufficient power for a specified confidence interval. Two examples in sport and exercise science are used to illustrate sample size determination for confidence intervals in the dependent and independent t tests. The relevant SAS code and output are provided with detailed annotations. As the use of confidence intervals becomes a more integral part of studies in sport and exercise science, the reporting and analysis of their power should be considered much like that of hypothesis tests.     

Two-stage screening for math problem-solving difficulty using dynamic assessment of algebraic learning

The purpose of this study was to explore the utility of a dynamic assessment (DA) of algebraic learning in predicting third graders' development of mathematics word-problem difficulty. In the fall, 122 third-grade students were assessed on a test of math word-problem skill and DA of algebraic learning. In the spring, they were assessed on word-problem performance. Logistic regression was conducted to contrast two models. One relied exclusively on the fall test of math word-problem skill to predict word-problem difficulty on the spring outcome (less than the 25th percentile). The second model relied on a combination of the fall test of math word-problem skill and the fall DA to predict the same outcome. Holding sensitivity at 87.5%, the universal screener alone resulted in a high proportion of false positives, which was practically reduced when DA was included in the prediction model. Findings are discussed in terms of a two-stage process for screening students within a responsiveness-to-intervention prevention model.