Monthly Summaries of Nursing Research: December, 2008

January 05, 2009

A New Computer Program Helps Researchers With Investigative Reviews

Kim J, Pressler SJ, Jones J, Graves JR. Generating scientific models of knowledge using arcs. Clin Nurse Spec. 2008;22:286-292.

The rapid increase and accessibility of published research data are valuable resources for scientists and clinicians. However, synthesizing this wealth of information requires substantial time to locate, read, and evaluate the growing body of literature now available from a variety of resources. A team of investigators looked at a computerized approach for managing investigative reviews of the nursing literature. Using a new software program (arcsc© Indianapolis, Indiana) designed to manage data and model empirical knowledge, the team examined whether a software program might be able to synthesize research literature and create scientific models based on a review of 10 descriptive studies of nutrition in heart failure.

The studies were summarized and manually coded into the software program by the team of investigators. Then, the program was run and the quality of the scientific models of knowledge created by the system evaluated. Following a review of the program's output, the team concluded that arcsc© was feasible for conducting integrative literature reviews. Unlike common statistical methods, the computer program categorized a number of variables and relationships found in the selected studies. It also provided a scientific model for further empirical testing and aggregated 104 dependent and 93 independent operational variables and numerous relationships among these variables. In fact, the software identified an undiscovered structural relationship between cachexia and 18-month mortality, independent of age or the New York Heart Association classification system of heart failure. This relationship could be tested by investigators as a new theoretical model.

Given the proliferation of descriptive research in nursing science, this new software program may be particularly useful in summarizing data on behavioral responses to health and illness, creating new scientific models, and identifying gaps and inconsistencies



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