Version 1
: Received: 5 August 2024 / Approved: 5 August 2024 / Online: 5 August 2024 (08:27:25 CEST)
How to cite:
M. Tabak, B.; Britto Froner, M.; C. de Almeida, R. D.; D. Couto, L. C.; Christiano Silva, T.; de Fatima Faria Barbosa, S. Health Literacy: Structural Topic Modeling and Citation Trends. Preprints2024, 2024080278. https://doi.org/10.20944/preprints202408.0278.v1
M. Tabak, B.; Britto Froner, M.; C. de Almeida, R. D.; D. Couto, L. C.; Christiano Silva, T.; de Fatima Faria Barbosa, S. Health Literacy: Structural Topic Modeling and Citation Trends. Preprints 2024, 2024080278. https://doi.org/10.20944/preprints202408.0278.v1
M. Tabak, B.; Britto Froner, M.; C. de Almeida, R. D.; D. Couto, L. C.; Christiano Silva, T.; de Fatima Faria Barbosa, S. Health Literacy: Structural Topic Modeling and Citation Trends. Preprints2024, 2024080278. https://doi.org/10.20944/preprints202408.0278.v1
APA Style
M. Tabak, B., Britto Froner, M., C. de Almeida, R. D., D. Couto, L. C., Christiano Silva, T., & de Fatima Faria Barbosa, S. (2024). Health Literacy: Structural Topic Modeling and Citation Trends. Preprints. https://doi.org/10.20944/preprints202408.0278.v1
Chicago/Turabian Style
M. Tabak, B., Thiago Christiano Silva and Sayonara de Fatima Faria Barbosa. 2024 "Health Literacy: Structural Topic Modeling and Citation Trends" Preprints. https://doi.org/10.20944/preprints202408.0278.v1
Abstract
Health literacy is a growing research area with specific aspects and different instruments to measure health literacy. This article uses natural language processing model to analyze the academic corpora regarding seven health literacy instruments - Health Literacy Questionnaire, Mental Health Literacy Scale, Rapid Estimate of Adult Literacy in Medicine, Test of Functional Health Literacy in Adults, Health Literacy Survey, The Newest Vital Sign and eHealth Literacy Scale. We apply Structural Topic Modeling to all the abstracts of the selected academic corpora, label the topics based on their focus, and use the topic distribution with metadata to train a Random Forest algorithm for predicting yearly citations. We estimate Regression models based on the ranking of the most relevant features, which serves as a robustness check and to infer their impact on citation dynamics. We have found that Digital Health Literacy is positively associated with yearly citations, while other topics such as Functional Health Literacy and Women's Health reduce citation likelihood. Other article characteristics also have shown a significant role in the citation likelihood, such as the publication year, amount of articles and certain keywords. These findings portray the current landscape of health literacy research, highlighting literature gaps and popular topics.
Keywords
Health Literacy; Mental Health Literacy; Digital Health Literacy; HLQ; HLS-EU; MHLS; REALM; TOFHLA; eHeals; NVS
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
