Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling

Abstract

PurposeTo establish a model of Japanese pharmacy students’ learning motivation profile and investigate the effects of pharmaceutical practical training programs on their learning motivation.MethodsThe Science Motivation Questionnaire II was administered to pharmacy students in their 4th (before practical training), 5th (before practical training at clinical sites), and 6th (after all practical training) years of study at Josai International University in April, 2016. Factor analysis and multiple-group structural equation modeling were conducted for data analysis.ResultsA total of 165 students participated. The learning motivation profile was modeled with 4 factors (intrinsic, career, self-determination, and grade motivation), and the most effective learning motivation was grade motivation. In the multiple-group analysis, the fit of the model with the data was acceptable, and the estimated mean value of the factor of ‘self-determination’ in the learning motivation profile increased after the practical training programs (P= 0.048, Cohen’s d= 0.43).ConclusionPractical training programs in a 6-year course were effective for increasing learning motivation, based on ‘self-determination’ among Japanese pharmacy students. The results suggest that practical training programs are meaningful not only for providing clinical experience but also for raising learning motivation.

Palestinian pharmacists’ knowledge of issues related to using psychotropic medications in older people: a cross-sectional study

“…Incorporating practical sessions in pharmacology and pharmacotherapy might have positive effects on the knowledge, attitudes, and skills of pharmacists [ 13 ]. Recently, it has been reported that practical training programs increased learning motivation among pharmacy students [ 14 ]. Similarly, using computer simulations was shown to enhance knowledge of medications and pharmacology [ 15 ].…”

Interesting statistics regarding the papers published in <i>Journal of Educational Evaluation for Health Professions</i> in 2017

“…The most frequently accessed article was “Reliability of a viva assessment of clinical reasoning in an Australian pre-professional osteopathy program assessed using generalizability theory” by Vaughan et al [ 1 ], which was published in January this year (accessed 11,913 times). The next most-viewed article was “Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling” by Yamamura and Takehira [ 2 ], which was published this February (accessed 11,339 times). This article was also the most downloaded article in 2017 (downloaded 223 times), and has already been cited by another article.…”

An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling

“…The authors were given permission to use the questionnaire for this study. The average score and standard deviation for each item and each school year among the students were expressed in a previous paper [ 2 ]. The students’ learning process was measured with the 20-item “revised two factor study process questionnaire (R-SPQ-2F)” [ 12 ], which was translated into Japanese.…”

“…As pharmacy education lasts 6 years, students must maintain strong motivation. In previous research, the authors noted a positive change in learning motivation based on self-determination and an inclination towards “deep learning” among students after the introduction of clinical clerkships [ 2 , 3 ]. The students were considered to be motivated during clinical clerkship and to understand the underlying concepts of pharmacy by deep learning [ 4 ].…”

“…In previous papers, we modeled the learning process and learning motivation profiles of Japanese pharmacy students independently [ 2 , 3 ]. In this report, the authors established a model demonstrating the causal relationship between the learning process and learning motivation profiles using structural equation modeling (SEM).…”