Factors Disrupting the Continuity of Care for Patients with Chronic Disease During the Pandemics: A Systematic Review
Health science reports(2024)
Shiraz Univ Med Sci | Flinders Univ S Australia
Abstract
Background and AimsContinuous routine care is necessary to prevent long-term complications of chronic diseases and improve patients' health conditions. This review study was conducted to determine the factors disrupting continuity of care for patients with chronic diseases during the pandemic.MethodsAll original articles published on factors disrupting continuity of care for patients with chronic disease during a pandemic between December 2019 and June 28, 2023, in PubMed, Web of Science, Scopus, and ProQuest databases were searched. Selection of articles, data extraction, and qualitative evaluation of articles (through STROBE and COREQ checklist) were done by two researchers separately. Data graphing form was used to extract the data of each study and then the data were classified by thematic analysis method.ResultsOut of 1708 articles reviewed from the databases, 22 were included. The factors disrupting the continuity of care for patients with chronic diseases during the epidemics were classified into two main categories: patient-side factors and health system-side factors. Patient-side factors including psychological, individual and social, disease-related, and health system-side factors including provider access, health system institutional, and infrastructural and financial problems were among the subcategories disrupting the continuity of care for patients with chronic diseases during the pandemic. Based on the studies, psychological factors and access to the provider were among the most frequent factors affecting the continuity of care for patients with chronic diseases in the pandemic.ConclusionConsidering the factors disrupting the continuity of care and applying appropriate interventions based on them, can guarantee the continuity of providing services to chronic patients in health crises.
MoreTranslated text
Key words
chronic disease,continuity of patient care,pandemics,systematic review
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2019
被引用125 | 浏览
2016
被引用204 | 浏览
2020
被引用3054 | 浏览
2020
被引用51 | 浏览
2020
被引用34 | 浏览
2020
被引用74 | 浏览
2020
被引用17 | 浏览
2021
被引用140 | 浏览
2021
被引用4 | 浏览
2021
被引用12 | 浏览
2021
被引用287 | 浏览
2021
被引用41 | 浏览
2021
被引用3 | 浏览
2022
被引用48 | 浏览
2022
被引用4 | 浏览
2022
被引用6 | 浏览
2022
被引用7 | 浏览
2022
被引用2 | 浏览
2023
被引用5 | 浏览
2023
被引用3 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined