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.

Studies have highlighted the importance of applying Behavioral Sciences interventions to develop equity in the prevention of chronic diseases in the public health domain. Our study aims to assess the evidence of this influence. We undertook a systematic review study using the electronic databases PubMed, Web of Science, Scopus and Cochrane, searching for work published between 2013 and 2023. The research analyzed the influence of Behavioral Sciences intervention studies on public health. This review was registered and published in PROSPERO, registration number CRD42023412377. The systematic search identified 2951 articles. The review analyzed 26 studies. The quality assessment of the articles showed an overall average of 74%, with the majority of studies being of high quality. The interventions with the best evidence for chronic diseases used framing messages, nudges and vouchers. Messages with incentives also showed satisfactory evidence. The most prevalent outcomes were related to screening tests and patient adherence to treatment. The current state of decision-making remains mainly at the patient level, with potential for further exploration of the roles of healthcare professionals and decision-makers in future research efforts. Limitations relate to the heterogeneity of the study sample, which hinders a more precise analysis of specific interventions and outcomes in chronic diseases.

Health literacy is essential for promoting well-being and the ability to make informed decisions. We investigated the level of health literacy in Brazil and identified the predictive factors that influence it. Our data contribute to the international context, with a focus on countries in the Global South and, in particular, Latin America. By analyzing health literacy in Brazil, this study sheds light on the challenges faced by populations with similar socioeconomic backgrounds in low- and middle-income countries, where disparities in access to education and health services are widespread. In addition to descriptive analysis, we used the Random Forest machine learning algorithm, which uses bootstrap aggregation (bagging). To make the results interpretable, we implemented Shapley’s Additive exPlanation values. The results show a predominance of problematic levels of health literacy among the population. The analysis reveals that factors such as medication use, dependence on the Unified Health System (Sistema Único de Saúde), and educational level are significant predictors of health literacy. The findings highlight the need for public policies aimed at reducing socioeconomic disparities and improving the public health system in order to promote better access to and understanding of health information.

Introduction: In recent decades, health literacy, in connection with a broad range of public 1 health terms, has become a burgeoning field. This study aims to explore trends and biases in this 2 area through a bibliometric analysis. Methods: A Random Forest Model was utilized to identify 3 keywords and other metadata that predict annual citations in the field. In order to supplement 4 this machine learning analysis, we have also implemented a bibliometric review of the corpus. 5 Results: Findings indicate a high positive coefficient for the keyword ’Covid-19’ and ’Male’, whereas a 6 negative coefficient was observed for ’Female’, suggesting potential biases. Evolving themes such as 7 Covid-19, Mental Health, and Social Media were discovered. A significant shift was noted in the main 8 publishing journals, while the major contributing authors remained the same. Discussion: The results 9 hint at the influence of the Covid-19 pandemic and potential gender biases on citation likelihood, as 10 well as changing publication strategies despite the fact that the main researchers remain as the ones 11 that have been studying health literacy since its creation.