Mathematical Methods in the Training of Future Psychologists, Pharmacists and Physicians: from Statistical Processing of Research Results to Building Mathematical Models of Phenomena and Processes

Source abstract

The paper substantiates an integrated approach to teaching mathematical methods to psychology students, future pharmacists and medical students, in which the mathematical apparatus appears not as a set of procedures detached from practice but as a working tool of a specialist – from the statistical processing of research results to the construction of mathematical models of phenomena, processes and states. The topic is relevant because evidence-based medicine, pharmacokinetics and modern psychological research rely on quantitative data, whereas students of non-mathematical specialities often perceive statistics as a formal discipline unrelated to their future profession. The aim of the paper is to reveal, on the basis of an analysis of professional publications, the content, sequence and didactic means of developing the skills to collect and evaluate quantitative data, to work with samples of measured values, to plan experiments, to test statistical hypotheses, to perform correlation and regression analysis, to forecast expected results and to build an analytical model of the phenomenon under study from a finite sample of experimental data. The study is theoretical and applied and has no empirical stage; the methods of analysis and synthesis, comparison, systematisation, modelling and the case method based on conditional data were used. Mathematical methods are systematised according to the typical professional tasks of the three specialities; a scheme of the end-to-end logic "experimental design – data collection – descriptive statistics – hypothesis testing – correlation and regression analysis – model and forecast", an algorithm for choosing a statistical test, the stages of building an analytical model and a level-based system of learning tasks are proposed. Using the examples of a psychological study of the relationship between anxiety and academic performance, a clinical comparison of two treatment regimens and a pharmacokinetic elimination model, the paper shows how descriptive statistics, hypothesis testing, correlation and regression analysis acquire professional meaning. The materials can be used to update course syllabi for mathematics and informatics-related disciplines – specifically those covering the statistical analysis of research results – at higher education institutions specializing in medicine, pharmacy, and psychology.