Assessment of computer and statistical literacy levels among medical students

Abstract

Objective. Assessing the level of statistical literacy of medical students in the context of the transition from clinical medicine to evidence-based medicine and the increased need for doctors who can correctly interpret data presented in various scientific sources and conduct their own research.Materials and methods. The research methods included an online survey and statistical processing of the results. Additionally, a business analysis service was used to validate the data and search for insights quickly. When statistically processing the results, descriptive statistics were used, and to compare indicators, non-parametric methods were used: Mann-Whitney and the Kruskal-Wallis test, which allow working with small samples, as well as one-way analysis of variance and correlation analysis. Confidence level of 0.05.Results. Two hundred fifty-two respondents participated in the study. The study revealed that medical students, on average, had high grades in both the computer science course (4.5±0.6 points) and the statistics course (4.4±0.6 points). The level of statistical literacy is high immediately after completing the course but decreases over several years of study. There was a significant average positive correlation between teaching GPA and ICT scores (r=0.35, p< 0.05) and a significant weak positive correlation between teaching GPA and statistics (r=0.23, p< 0.05), and an average correlation between statistics scores and ICT (r=0.59, p< 0.05). More than half (54 %) of medical students wanted to undergo more in-depth training in the field of ICT, while 35 % of medical students wanted to study programing languages in depth, business analytics tools were in second place, and 49 % of students showed an interest in the in-depth study of statistics medical specialties.Conclusion. There is a gap between students’ knowledge of statistics, computer science, and digitalization and the level of proficiency required of a specialist when entering the workforce. Its reduction is possible through students’ access to real clinical practice data throughout the entire learning process, the development of additional training programs dedicated to data analysis in medicine, and the development of a culture of working with data from the first year, including using business analytics tools and special attention focusing on competent data visualization.