Effectiveness of Interprofessional Learning and Simulation on Healthy Aging in Undergraduate Medical and Nursing Program

Overview

Aging population is an important public health issue and require coordinated and comprehensive response. Medical and nursing schools need to address challenges in health care delivery, and interprofessional simulation-based education (IPSE) provides realistic learning experiences in which interprofessional communication, roles and teamwork can be developed and assessed. The study aims to examine the effectiveness of delivering an IPSE program versus traditional course to nursing and medical students. The primary outcome is improved communication skills, assessed by Communication Skill Attitude Scale (CSAS) divided in two subscales: Positive Attitude Scale (PAS) and Negative Attitude Scale (NAS).

Design: this is a randomized, controlled study. Eligible participants will be randomly allocated to intervention group (IPSE) or control group (traditional course) in a 1:1 ratio. The investigator will use a block size of four with no stratification: for each block of 4 students, a different random ordering of 2 assignments to each treatment will be produced. A table of random numbers will be used to produce 2 randomization lists: one for nursing students and one for medical students. Setting and participants: the study will be performed at the Università del Piemonte Orientale (Italy). In this study, second-year student volunteers from the Nursing School and fourth and fifth-year student volunteers from the School of Medicine will be recruited through an information session. Intervention: IPSE program is divided in two phases: 1) a self-study course will be offered about lifestyle modification, 2) four different learning methods: didactic lecture, role playing, standardized patient and a new immersive advanced simulation learning environment will be provided. Data collection: demographic data (gender, age, education) will be gathered to obtain a basic profile of participants prior to allocation. Before the randomization and after the completion of the program, each participant will complete the evaluation session. Sample size calculation: a minimal total study sample size of 60 (24 nursing and 36 medical students) would be required to provide 80% power to reject the null hypothesis that no difference existed between the two research arms in NAS/PAS score, with a two-sided type 1 error of 5%. To balance the sample size of the two groups, the investigator decided to recruit the same number of nursing and medical students: 36 participants for each group. With a 10% allowance for students lost at follow up, a sample size of 80 students would be required and will be used. Analyses: all analyses will be based on the Intention-To-Treat (ITT) approach. Students who do not start intervention or only complete the 25% of the course will not be included in the analysis. Descriptive statistical analyses will be conducted separately for each student group using the information collected at baseline. For categorical variables, number and percentage of participants in each category will be reported while, for normally distributed continuous variables, the mean and Standard Deviation values will be calculated. If the data are not normal, the investigator will use median and interquartile range. Subsequently, the analyses will be conducted categorizing the continuous variables and joining levels of ordinal variables to avoid the occurrence of scattered data phenomena. In order to evaluate baseline differences between nursing and medical students, approximate (Chi square) or exact (Fisher) association tests will be performed for categorical variables while, for numeric ones, the investigator will use the independent T-test(parametric) or the Mann-Whitney U test (nonparametric). The obtained p value values will be reported. The individual response profiles of primary outcomes will be implemented. Then, subjects will be categorized into two groups: successful if they increase positive scores/decrease negative scores and unsuccessful if they decrease positive scores/increase negative scores. The association with these binary outcomes and the recorded variables will be evaluated with appropriate tests (Chi-square, Fisher Exact, T, Mann-Withey). For each outcome the Relative Risks will be calculated with the respective 95% Confidence Interval using a Poisson regression with a robust error variance. Study data will gather and manage with REDCap31 electronic data-capture tools, and the analyses will be performed by using SAS version 9.4.