Learning effects of aging simulation­-based training Analysis using text mining

In order to serve the increasing elderly population, gerontological dental education is recently being given to provide understanding of the needs of the elderly, and nurture healthcare professionals who can provide reliable and safe services for them. In order to actively communicate with elderly patients, and clarify related challenges, it is necessary for healthcare professionals to adopt detailed support measures for them, and show empathy based on their understanding of the elderly, their changing needs and their own self­awareness. The recent in­ crease in the number of nuclear families has mark­ edly reduced opportunities for the younger genera­ tion to communicate with the elderly, making it diffi­ cult to understand their needs and emotions. Al­ though students learn about age­related physical and psychological changes through school lectures, such knowledge on its own is insufficient to under­ stand the elderly, and develop empathy for them. Experience­based learning has been reported ef­ fective in enhancing understanding of their needs. Aging simulation­based training, which provides an opportunity to experience physical changes in the elderly, has been suggested to promote psychologi­ cal empathy and provide an opportunity to learn how to support their needs. For these reasons, it has been actively adopted in nursing education to promote smooth communication with the elderly. An aging simulation­based training program was launched in the Department of Geriatric Dentistry at Osaka Dental University in fiscal year 2001 as part of preclinical practice prior to clinical training in hos­ pitals for fourth­year students who have completed their basic dental education. We adopted natural language analysis techniques to examine the use­ fulness of this training by analyzing free descrip­ tions submitted by students after completion of the program through text mining. Learning effects of aging simulation­based training Analysis using text mining