The Intersection of Health Literacy and Public Health: A Machine Learning-Enhanced Bibliometric Investigation

Abstract

In recent decades, health literacy has garnered increasing attention alongside a variety of public health topics. This study aims to explore trends in this area through a bibliometric analysis. A Random Forest Model was utilized to identify keywords and other metadata that predict average citations in the field. To supplement this machine learning analysis, we have also implemented a bibliometric review of the corpus. Our findings reveal significant positive coefficients for the keywords “COVID-19” and “Male”, underscoring the influence of the pandemic and potential gender-related factors in the literature. On the other hand, the keyword “Female” showed a negative coefficient, hinting at possible disparities that warrant further investigation. Additionally, evolving themes such as COVID-19, mental health, and social media were discovered. A significant change was observed in the main publishing journals, while the major contributing authors remained the same. The results hint at the influence of the COVID-19 pandemic and a significant association between gender-related keywords on citation likelihood, as well as changing publication strategies, despite the fact that the main researchers remain those who have been studying health literacy since its creation.

Public Telemedicine Policy in Brazilian Unified Health System: An Impact Analysis

“…Qualitative research already carried out, as mentioned in this article, has shown that healthcare professionals, policymakers, and telemedicine users await adequate regulation to ensure the safety of patients and physicians. See Silva et al [ 52 ] for a discussion on forecasting the pandemic and Tabak et al [ 53 ] for discussing health literacy and public health literature. Further research could explore how telemedicine helped reduce or limit contagion during the pandemic and the effects health literacy has on the effectiveness of telemedicine.…”