Southern Illinois University Carbondale

About: Southern Illinois University Carbondale is a education organization based out in Carbondale, Illinois, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13570 authors who have published 24819 publications receiving 667385 citations. The organization is also known as: SIU Carbondale & SIUC.

Validity of a verbally administered numeric rating scale to measure cancer pain intensity

Abstract: The ability to quantify pain intensity is essential when caring for individuals in pain in order to monitor patient progress and analgesic effectiveness. Three scales are commonly employed: the simple descriptor scale (SDS), the visual analog scale (VAS), and the numeric (pain intensity) rating scale (NRS). Patients with English as a second language may not be able to complete the SDS without translation, and visually, cognitively, or physically impaired patients may have difficulty using the VAS. The NRS has been found to be a simple and valid alternative in some disease states; however, the validity of this scale administered verbally, without visual cues, to oncology patients has not yet been established. The present study examined validity of a verbally administered 0-10 NRS using convergence methods. The correlation between the VAS and the NRS was strong and statistically significant (r = 0.847, p < 0.001), supporting the validity of the verbally administered NRS. Although all subjects were able to complete the NRS and SDS without apparent difficulty, 11 subjects (20%) were unable to complete the VAS. The mean opioid intake was significantly higher for the group that was unable to complete the VAS (mean 170.8 mg, median 120.0 mg, SD = 135.8) compared to the group that had no difficulty with the scale (mean 65.6 mg, 33.0 mg, SD = 99.7) (Mann-Whitney test, p = 0.0065). The verbally administered 0-10 NRS provides a useful alternative to the VAS, particularly as more contact with patients is established via telephone and patients within the hospital are more acutely ill.

Toward a unified theory of consumer acceptance technology

Abstract: In the last few decades, scholars and practitioners have increasingly tried to understand the factors that influence technology acceptance. Theories and models developed by scholars have tended to focus on the role of cognition and have rarely included affect. The few studies that have incorporated affect have tended to measure a single emotion rather than modeling it comprehensively. This research addresses that inadequacy in our understanding of technology adoption by merging two previously unrelated models: TAM (the Technology Acceptance Model) and PAD (the Pleasure, Arousal, and Dominance paradigm of affect). This study also examines an enhanced view of cognition. The product of this unified theoretical framework is referred to as the Consumer Acceptance of Technology (CAT) model. The results of a test using structural equation modeling provide empirical support for the model. Overall, the CAT model explains over 50% of the variance in consumer adoption intentions, a considerable increase compared to TAM. These findings suggest that substantial improvement in the prediction of technology adoption decisions is possible by use of this model with its integration of affect and cognition. © 2007 Wiley Periodicals, Inc.

Propensity scores: an introduction and experimental test.

Abstract: Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment on which the two groups differ. This article presents a general introduction to propensity score analysis, provides an example using data from a quasi-experiment compared to a benchmark randomized experiment, offers practical advice about how to do such analyses, and discusses some limitations of the approach. It also presents the first detailed instructions to appear in the literature on how to use classification tree analysis and bagging for classification trees in the construction of propensity scores. The latter two examples serve as an introduction for researchers interested in computing propensity scores using more complex classification algorithms known as ensemble methods.