RESUMO

Introduction: Julia is a widely used open-source programming language. It is employed in big data modeling, drug development, electronic design, and artificial intelligence applications. Julia was introduced in 2012, and it has gained recognition for its versatility, scalability, and fast performance, especially in fields like machine learning, data science, and scientific computing, compared to other computational methods. Concurrently, computational literacy has emerged in healthcare education as a framework for navigating the digital era through interdisciplinary problem-solving. It reflects the practical knowledge of computational thinking and technology-driven approaches. Leveraging Julia's capabilities, we inquire about its use for computational literacy in healthcare education. Objective: This literature review aims to explore Julia as first programming language for computational literacy in health education. Methodology: Rapid evidence assessment is a streamlined version of systematic reviews, aiming to balance time constraints with minimizing bias. This abstract shows the preliminary results of an ongoing rapid literature review. The research protocol was oriented by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Journal and Conference Abstracts guidelines and registered on the Open Science Framework. The Population-Concept-Context method (computational literacy, Julia, health education) guided the formulation of the search strategy and primary review question as "What role does the Julia programming language play as a computational literacy method for health education?". We conducted electronic literature searches on the ACM Digital Library and PubMed databases from on January 2024. The search terms constructed referenced from the Medical Subject Headings were ("literacy" OR "computing methodologies" OR "education") AND "Julia". To mitigate potential bias, two trained researchers were entrusted to conducting the search and selection process. This summary was restricted to full articles without language filters published after 2014, and excluding gray-unconventional literature or books/documents. We performed a narrative synthesis, categorized the included articles by study type, and supplemented our analysis with the snowball method. Articles were selected through the Rayyan platform, any contentious items were deliberated until consensus. The retrieval process resulted in 770 articles (ACM) and 90 (PubMed). No additional article was included from the reference lists and 12 duplicates were excluded. Results: 10 articles were selected for full-text evaluation, of which two theoretical studies were considered for synthesis. The articles stated Julia was as a computational method for high performance and level of abstraction of problems in the biomedical field. Julia semantics and syntax is scalable for parallel and high performance computing. It fosters data and quantitative research methods in health more computational. Julia’ versatility favors a top-down literacy approach whereby computational thinking is intuitively built, and the generalization of algorithm development for solving real problems through computational methods. Conclusion: We suggest Julia as first programming language in computational literacy for health education due to its potential for enhancing computational thinking and abstraction. Comparative studies between Julia and other computational literacy approaches are needed.