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Prediction Model of the Quality of Life for Patients with Pancreatic Cancer

This study attempted to establish a predictive model that systematically and comprehensively predicts the quality of life (QoL) of patients with pancreatic cancer. The study used a descriptive cross-sectional design. A total of 248 patients was included who were selected via the convenience sampling...

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Detalles Bibliográficos
Autor principal: Lee, Jisun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602008/
https://www.ncbi.nlm.nih.gov/pubmed/36292420
http://dx.doi.org/10.3390/healthcare10101973
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author Lee, Jisun
author_facet Lee, Jisun
author_sort Lee, Jisun
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description This study attempted to establish a predictive model that systematically and comprehensively predicts the quality of life (QoL) of patients with pancreatic cancer. The study used a descriptive cross-sectional design. A total of 248 patients was included who were selected via the convenience sampling method. A structured questionnaire was used and the collected data were analyzed for fitness, using the SPSS WIN 25.0 program and AMOS 24.0. The perceived health status of the patients with pancreatic cancer would directly affect their QoL and indirectly influence the QoL through social support, symptoms, functional status, and age. The application of social support intervention programs to relieve specific symptoms and improve the functional status according to the conditions of patients may contribute to improving the QoL of pancreatic cancer patients. This predictive model could be used as the basis for developing a nursing intervention plan and writing evaluation guidelines for practitioners who provide nursing care for patients with pancreatic cancer.
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spelling pubmed-96020082022-10-27 Prediction Model of the Quality of Life for Patients with Pancreatic Cancer Lee, Jisun Healthcare (Basel) Article This study attempted to establish a predictive model that systematically and comprehensively predicts the quality of life (QoL) of patients with pancreatic cancer. The study used a descriptive cross-sectional design. A total of 248 patients was included who were selected via the convenience sampling method. A structured questionnaire was used and the collected data were analyzed for fitness, using the SPSS WIN 25.0 program and AMOS 24.0. The perceived health status of the patients with pancreatic cancer would directly affect their QoL and indirectly influence the QoL through social support, symptoms, functional status, and age. The application of social support intervention programs to relieve specific symptoms and improve the functional status according to the conditions of patients may contribute to improving the QoL of pancreatic cancer patients. This predictive model could be used as the basis for developing a nursing intervention plan and writing evaluation guidelines for practitioners who provide nursing care for patients with pancreatic cancer. MDPI 2022-10-09 /pmc/articles/PMC9602008/ /pubmed/36292420 http://dx.doi.org/10.3390/healthcare10101973 Text en © 2022 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Jisun
Prediction Model of the Quality of Life for Patients with Pancreatic Cancer
title Prediction Model of the Quality of Life for Patients with Pancreatic Cancer
title_full Prediction Model of the Quality of Life for Patients with Pancreatic Cancer
title_fullStr Prediction Model of the Quality of Life for Patients with Pancreatic Cancer
title_full_unstemmed Prediction Model of the Quality of Life for Patients with Pancreatic Cancer
title_short Prediction Model of the Quality of Life for Patients with Pancreatic Cancer
title_sort prediction model of the quality of life for patients with pancreatic cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602008/
https://www.ncbi.nlm.nih.gov/pubmed/36292420
http://dx.doi.org/10.3390/healthcare10101973
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