This paper introduces and validates a comprehensive instrument designed to measure financial literacy, its underlying determinants, and to assess how factors such as planning affect financial vulnerability and fraud in Brazil. This work represents a crucial step toward achieving several Sustainable Development Goals (SDGs). The study utilizes a two-fold methodology. First, Confirmatory Factor Analysis (CFA) is used to validate a six-component model consisting of Financial Literacy, Vulnerability, Fraud, Cognitive Reflection, Crypto Literacy, and Planning. This analysis is followed by the development and interpretation of a Random Forest model, which was identified as the best-performing predictor in a comparison of seven machine learning algorithms. The CFA results showed that Financial Planning has a stronger negative correlation with Financial Vulnerability (−0.642) and Fraud (−0.375) than Financial Literacy does. This evidence was further supported by the machine learning analysis; analyses using both SHAP and LIME identified Financial Planning as the strongest predictor of financial vulnerability and fraud. The analysis further showed significant social inequalities in the developed models and identified the gender variable (female) as an important predictor of enhanced financial vulnerability. Converging evidence from both CFA and machine learning confirms that sound planning practices are more important than financial knowledge in reducing financial distress. Our findings provide a solid foundation for the development of inclusive public policy that promotes behavioral change, aiming to reduce systemic inequalities (SDG 10) and achieve sustainable economic stability (SDG 8), thereby supporting social goals and the Sustainable Development Goals.

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.

Environmental health literacy (EHL) is essential for individuals to protect themselves from environmental health risks. Indigenous populations are particularly vulnerable to these risks, given the historical threats they have suffered from the advance of agricultural frontiers and impacts of deforestation, mining, and extreme weather events. This study investigates the dimensions of EHL among indigenous communities in Brazil, considering the scarcity of research in this field. Using a scale adapted to measure EHL in topics such as air, water, and food, it was possible to access the EHL levels of a sample of different Brazilian indigenous ethnic groups. Statistical analysis included descriptive methods and the Wilcoxon and Kruskal–Wallis tests. The results revealed significant variations in EHL levels, influenced by factors such as gender, place of residence, age, education, access to health services, and potable water. In addition, the presence of traditional actors, such as midwives, was identified as an important factor in the transmission of health knowledge. The research highlights the need for public policies that respect the cultural specificities of indigenous communities and promote self-care and environmental preservation, contributing to the development of culturally sensitive public health strategies.

Financial literacy is essential for sustainable social and economic development, but several demographics and financial behaviors can influence informed decision-making. In this investigation, we examine the interaction between Financial Literacy (FL) and Financial Wellbeing and Behavior (FWB) across 286 Brazilian education professionals. We employ a two-step approach for analysis: step one is determining the influential demographic variables for Financial Literacy (FL); in step two, FL serves as a primary variable for explaining Financial Wellbeing and Behavior (FWB). We utilize both multiple regressions and supervised machine learning for factor verification for both concepts. The findings show that superior Financial Literacy is a highly significant and substantial positive antecedent of excellent Financial Wellbeing and Behavior ($p<0.01$). We also find that a significant gender gap exists in financial knowledge, such that women significantly underperform men on financial literacy tests. Machine learning models confirm that FL is the strongest feature for FWB prediction by far and that gender is the strongest antecedent of a consumer’s level of financial literacy. These findings suggest that policymakers must do more than provide primary financial literacy; they also need to deploy behavioral insights to raise long-term financial freedom and close the gender gap. Upscaling teacher financial literacy is a strategic social sustainability investment that promotes the explicit Sustainable Development Goals, including “Quality Education,” “Gender Equality,” “Decent Work” and “Economic Growth”.