Healthy Aging Clinical Trial
Official title:
Interprofessional Learning and Simulation Versus Traditional Course on Healthy Aging: a Randomized Control Trial
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.
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