Predicting health literacy in Brazil: a machine learning approach.

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...

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Publicado en:Health Promotion International Vol. 40; no. 3; pp. 1 - 11
Autores principales: Tabak, Benjamin Miranda, Almeida, Rubiane Daniele Cardoso de, Froner, Matheus Britto, Cardoso, Débora Helena Rosa, Conceição, Laís Almeida da
Formato: research tables/charts Journal Article
Publicado: Oxford University Press / USA Jun2025
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Predicting health literacy in Brazil: a machine learning approach.
      aug:
        au:
          Tabak, Benjamin Miranda
          Almeida, Rubiane Daniele Cardoso de
          Froner, Matheus Britto
          Cardoso, Débora Helena Rosa
          Conceição, Laís Almeida da
        affil: School of Public Policy and Government, Getulio Vargas Foundation, SGAN 602 Módulos A,B,C, Asa Norte, Brasília 70830-020, Brazil
      sug:
        subj:
          Health Literacy Evaluation
          Prediction Models
          Machine Learning
          Human
          Brazil
          Funding Source
          Descriptive Statistics
          Algorithms
          Medication Compliance
          Educational Status
          Health Information
          Questionnaires
          Public Health
          Health Policy
          Health Promotion
          Health Services Accessibility
          Social Determinants of Health
          Adolescence
          Adult
          Middle Age
          Aged
          Male
          Female
          Adolescent: 13-18 years
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
          Female
      ab: 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.
      pubtype: Academic Journal
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        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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