Objective This study focused on township hospitals in the cold regions of China and aimed to evaluate patient satisfaction during the medical care process. This study also discusses the correlation between patient needs and satisfaction. Background Hospitals seek to improve patient satisfaction to provide better service. However, there is a lack of existing literature on grassroots medical institutions in towns and townships, especially in cold regions. Therefore, this study aimed to examine the correlation between patient needs and the satisfaction of township hospitals in the cold regions of China. Methods First, a hierarchical task analysis method was used to build the hierarchy for patient satisfaction demands. Patients from 15 township hospitals in cold areas were subjected to semistructured interviews, and a theoretical model was proposed using the grounded theory method. Finally, each open code index was evaluated, and 270 questionnaires were issued to evaluate patient satisfaction. Results The framework for patient satisfaction demands included five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. A theoretical model for patient satisfaction demands was built, and four selective codes, including “Characteristic”, “Perceived Quality”, “Loyalty Intention”, and “Environment Expectation”, were extracted. The weights of these satisfaction-influencing factors were subsequently evaluated. Conclusions This study summarizes the existing problems in a basic health service provision capacity, climate adaptability, lack of environmental design, and so on; proposes four influencing factors; establishes a patient satisfaction evaluation model; and obtains the weight of influence of each factor. These results will help provide accurate and effective suggestions for hospital management.
In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.
Objective. Ovarian low-grade serous carcinomas are thought to evolve in a stepwise fashion from ovarian epithelial inclusions, serous cystadenomas, and serous borderline tumors. Our previous study with clinicopathological approach showed that the majority ovarian epithelial inclusions are derived from the fallopian tubal epithelia rather than from ovarian surface epithelia. This study was designed to gain further insight into the cellular origin of ovarian low-grade serous carcinomas by differential gene expression profiling studies. Methods. Gene expression profiles were studied in 43 samples including 11 ovarian low-grade serous carcinomas, 7 serous borderline tumors, 6 serous cystadenomas, 6 ovarian epithelial inclusions, 7 fallopian tubal epithelia, and 6 ovarian surface epithelia. Comprehensive analyses with hierarchical clustering, Rank-sum analysis and Pearson correlation tests were performed. Final validation was done on selected genes and corresponding proteins. Results. The gene expression profiles distinguished ovarian low-grade serous carcinomas from ovarian surface epithelia, but not from fallopian tubal epithelia cells. Hierarchical clustering analysis showed ovarian serous tumors and ovarian epithelial inclusions were clustered closely in a branch, but separated from ovarian surface epithelia. The results were further validated by selected proteins of OVGP1, WT-1, and FOM3, which were highly expressed in the samples of the fallopian tube, ovarian epithelial inclusions, and ovarian serous tumors, but not in ovarian surface epithelia. The reverse was true for the protein expression patterns of ARX and FNC1. Conclusions. This study provides evidence in a molecular level that ovarian low-grade serous carcinomas likely originate from the fallopian tube rather than from ovarian surface epithelia. Similar gene expression profiles among fallopian tube, ovarian epithelial inclusions, and serous tumors further support that ovarian low-grade serous carcinomas develop in a stepwise fashion. Such findings may have a significant implication for “ovarian” cancer-prevention strategies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.