Credit achievement ability during distance learning era: the case of Statistics in Medicine course

AIM In this study, the effects of the DL on academic career were investigated. BACKGROUND Distance Learning (DL) became mandatory in Italy from March 2020, due to COVID19 emergency. DESIGN The performances of students in Medical Statistics course of the Nursing degree in three campus of the University of Turin (Aosta, Beinasco and Cuneo) in the Academic Years 2019-2020 and 2020-2021 were considered. METHODS The study is based on 308 students, 48% of whom both attended the lessons and took the exams in DL. The effect of DL on student’s performance was evaluated using Logistic regression models and the results are showed in terms of odds ratios adjusted for gender, age and campus. RESULTS The results show that DL did not bring particular limitations to the students, highlighting on the contrary evident benefits in terms of organization and management of lessons and exams. Moreover, the level of students’ satisfaction at the end of the course increased in DL. CONCLUSION DL seems to do not affect the student’s ability on achieve credits, at least in mathematical subjects. More investigations are needed considering all courses’ types

A new numerical method for processing longitudinal data: Clinical applications

Background: Processing longitudinal data is a computational issue that arises in many applications, such as in aircraft design, medicine, optimal control and weather forecasting. Given some longitudinal data, i.e. scattered measurements, the aim consists in approximating the parameters involved in the dynamics of the considered process. For this problem, a large variety of well-known methods have already been developed. Results: Here, we propose an alternative approach to be used as effective and accurate tool for the parameters fitting and prediction of individual trajectories from sparse longitudinal data. In particular, our mixed model, that uses Radial Basis Functions (RBFs) combined with Stochastic Optimization Algorithms (SOMs), is here presented and tested on clinical data. Further, we also carry out comparisons with other methods that are widely used in this framework. Conclusion: The main advantages of the proposed method are the flexibility with respect to the datasets, meaning that it is effective also for truly irregularly distributed data, and its ability to extract reliable information on the evolution of the dynamics