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.

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Stored only when both papers are in the corpus — 13 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.