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A Microlearning-Based Self-directed Learning Chatbot on Medication Administration for New Nurses: A Feasibility Study.
Kim, Ae Ran; Park, Ae Young; Song, Soojin; Hong, Jeong Hee; Kim, Kyeongsug.
  • Kim AR; Author Affiliations: Department of Nursing, Samsung Medical Center (Drs A. R. Kim, Hong, and K. Kim, and Mss Park and Song); and Graduate School of Clinical Nursing Science, Sungkyunkwan University (Drs Hong and K. Kim), Seoul, Korea.
Comput Inform Nurs ; 2024 Mar 05.
Article en En | MEDLINE | ID: mdl-38453464
ABSTRACT
New nurses must acquire accurate knowledge of medication administration, as it directly affects patient safety. This study aimed to develop a microlearning-based self-directed learning chatbot on medication administration for novice nurses. Furthermore, the study had the objective of evaluating the chatbot feasibility. The chatbot covered two main topics medication administration processes and drug-specific management, along with 21 subtopics. Fifty-eight newly hired nurses on standby were asked to use the chatbot over a 2-week period. Moreover, we evaluated the chatbot's feasibility through a survey that gauged changes in their confidence in medication administration knowledge, intrinsic learning motivation, satisfaction with the chatbot's learning content, and usability. After using the chatbot, participants' confidence in medication administration knowledge significantly improved in all topics (P < .001) except "Understanding a concept of 5Right" (P = .077). Their intrinsic learning motivation, satisfaction with the learning content, and usability scored above 5 out of 7 in all subdomains, except for pressure/tension (mean, 2.12; median, 1.90). They scored highest on ease of learning (mean, 6.69; median, 7.00). A microlearning-based chatbot can help new nurses improve their knowledge of medication administration through self-directed learning.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article