Kent State University

About: Kent State University is a education organization based out in Kent, Ohio, United States. It is known for research contribution in the topics: Liquid crystal & Population. The organization has 10897 authors who have published 24607 publications receiving 720309 citations. The organization is also known as: Kent State & KSU.

Access to health care. A conceptual framework and its relevance to health care planning

Abstract: Despite some serious past efforts to clarify its multiple dimensions and meanings, access to health care has remained a rather elusive concept, hampering the work of health care policymakers and professionals as they endeavor to effect meaningful health care reform. This article provides perhaps the most detailed clarification of the access concept, especially the crucial linkages among the various access dimensions, and presents a comprehensive conceptual framework for evaluation and planning activities as they relate to people's access to health care services. The proposed conceptual model recognizes access as the outcome of a process involving the interplay between the characteristics of the health care service system and of potential users in a specified area, and moderated by health care related public policy and planning efforts. An elaborate typology of access, incorporating four pairs of access dimensions, is also derived. This atomization of the concept allows us to focus on specific aspects of the access to health care problem, and to develop precise outcome indicators of health system performance for evaluative purposes. Further, it enables the access concept and its pertinent dimensions to be put into proper perspective when assessing the health care access situation in a specific national or regional context. The relevance of the proposed access model and the typology to health care planning in general, and to spatial planning of health care service systems in particular, is also discussed.

Cannabis as a Substitute for Opioid-Based Pain Medication: Patient Self-Report

Abstract: Introduction: Prescription drug overdoses are the leading cause of accidental death in the United States. Alternatives to opioids for the treatment of pain are necessary to address this issue. Cannabis can be an effective treatment for pain, greatly reduces the chance of dependence, and eliminates the risk of fatal overdose compared to opioid-based medications. Medical cannabis patients report that cannabis is just as effective, if not more, than opioid-based medications for pain. Materials and Methods: The current study examined the use of cannabis as a substitute for opioid-based pain medication by collecting survey data from 2897 medical cannabis patients. Discussion: Thirty-four percent of the sample reported using opioid-based pain medication in the past 6 months. Respondents overwhelmingly reported that cannabis provided relief on par with their other medications, but without the unwanted side effects. Ninety-seven percent of the sample “strongly agreed/agreed” that they are able to decreas...

Time series forecasting with neural network ensembles: an application for exchange rate prediction

Abstract: This paper investigates the use of neural network combining methods to improve time series forecasting performance of the traditional single keep-the-best (KTB) model. The ensemble methods are applied to the difficult problem of exchange rate forecasting. Two general approaches to combining neural networks are proposed and examined in predicting the exchange rate between the British pound and US dollar. Specifically, we propose to use systematic and serial partitioning methods to build neural network ensembles for time series forecasting. It is found that the basic ensemble approach created with non-varying network architectures trained using different initial random weights is not effective in improving the accuracy of prediction while ensemble models consisting of different neural network structures can consistently outperform predictions of the single ‘best’ network. Results also show that neural ensembles based on different partitions of the data are more effective than those developed with the full training data in out-of-sample forecasting. Moreover, reducing correlation among forecasts made by the ensemble members by utilizing data partitioning techniques is the key to success for the neural ensemble models. Although our ensemble methods show considerable advantages over the traditional KTB approach, they do not have significant improvement compared to the widely used random walk model in exchange rate forecasting.