This study analyzes the keywords of health literacy research, network structure and attributes, characteristics of subgroups, and association by research period. The current research conducted a semantic network analysis on 3,971 papers published in PubMed (https://pubmed.ncbi.nlm.nih.gov/) between 1985 and 2020. Seventy-one keywords were selected from 3,284 words. To visualize the network structure of health literacy research, analyze subgroups, and carry out QAP analysis, data matrices of 71*71 was prepared in Ucinet and Netdraw software. Spring-embedded algorithms and degree centrality visualized the network structure. For the sub-group analysis, we employed the modularity using NCD (Newman Community Detection) algorithm. The keywords such as patient (798 times), health (777 times), mental (360 times), adult (323 times), and low (249 times) appeared to be frequent in the health literacy research. Additionally, considering the degree centrality, such keywords as patients, assessments, education, health, low, adult, care, use, improvement, and formation played an important role around health literacy in the overall network structure. We found that the subgroup was best classified as three groups. The first group includes keywords such as develop, health, educate, mental, and formation, and was named Development and Evaluation of Health Literacy Measurement Scale. In the second group, behavior, status, treatment, and adult became the keywords and they named it Effects and Outcomes of Health Literacy. The third group contained keywords including, risk, women, cancer, Chinese, and quality, and was named 'Application and Use of Various Targets of Health Literacy.
Key words: Health Literacy, Semantic Network Analysis, Subgroup Analysis, QAP, PubMed
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