Analyzing Community Care Research Trends Using Text Mining

Abstract

Purpose

This study utilized text mining to analyze research trends around community care, which focuses on improving patients’ quality of life by lessening the financial burden on caregivers and relieving patient discomfort.

Methods

To examine research trends by community care stage, Section 1 is set from 2017 to 2019, when the community care was implemented, and Section 2 from 2020 to 2021, after the end of the community care. Papers used for the analysis were extracted using the Korea Citation Index (KCI); a total of 132 articles were selected and subjected to text mining analysis.

Results

First, the main community care research areas included work, housing, economy, disability, and mind. Second, from 2017 to 2019, there was considerable interest in community care centered on households, and main keywords, such as nursing, family, and experience, appeared. Third, from 2020 to the present, there was high interest in community care centered on disabilities, and keywords, such as space, business, and Seoul City, appeared.

Conclusion

The results reveal the changing issues, with the implementation of community care. Overall, research has tended to focus on social and welfare systems, rather than health and medical systems. In the future, local, community-integrated health and medical care systems should be restructured and regional delivery systems established to make them more accessible.

Keywords:

• community care
• integrated care
• local community
• elderly care
• text mining
• network analysis

Abbreviations

WHO, World Health Organization; COVID-19, coronavirus disease-2019; AIP, aging in place; LDA, Latent Dirichlet Allocation; KCI, Korea Citation Index.

Data Sharing Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Consent for Publication

All authors approve the publication of this study. No other approval is needed.

Acknowledgments

We are grateful to the journal editors and anonymous reviewers for their time and helpful comments to improve the paper.

Disclosure

The authors declare no conflicts of interest.

Additional information

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021R1I1A4A01057428) and Bio-convergence Technology Education Program through the Korea Institute for Advancement Technology(KIAT) funded by the Ministry of Trade, Industry and Energy (No. P0017805)..