Patient simulator to drive diagnostic thinking process and deep learning

  • Or, P. P. L. The Education University of Hong Kong
  • So, H. C.F The Education University of Hong Kong
  • Leung C.L.C School of Nursing
  • Chung, J.W.Y The Education University of Hong Kong
Keywords: Deep Learning,, Nursing Education,, Study Approach,, Patient Simulator,, Patient Safety,, Diagnostic Thinking Process,, Think Aloud

Abstract

Background
The aim of this study was to evaluate clinical reasoning of the diagnostic thinking process among
student nurses in pre-enrollment nursing programs in two different educational institutions using
a patient simulator.
Method
This study adopted a quasi-experimental design. Problem behavior graphs (PBGs) were drawn
to reflect the participants’ clinical reasoning processes. Biggs’ Study Process Questionnaire
(SPQ) was used to evaluate their study approaches into six sub-scales.
Results
Forty-two subjects were randomly drawn from students of two pre-enrollment nursing programs:
21 from a college and 21 from a hospital-based nursing school. Subjects from the college had
more data driven strategy (53%) in the problem behavior graphs (PBGs). They used more surface
strategy than subjects from the nursing school (47%).
Conclusions
Results pinpoint data-driven strategy in nursing education helps to improve the current nursing
education in Hong Kong. Only if the ability to master diagnostic thinking process from student
nurses ensures patient safety in clinical environments.



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Author Biographies

Or, P. P. L., The Education University of Hong Kong

. Assistant Professor, 

Department of Health and Physical Education,

So, H. C.F, The Education University of Hong Kong

Department of Health and Physical Education

Leung C.L.C, School of Nursing

Grantham Hospital, Hong Kong

Chung, J.W.Y, The Education University of Hong Kong

Faculty of Liberal Arts and Social Sciences,

Published
2017-07-31
How to Cite
P. L., O. P., C.F, S. H., C.L.C, L., & J.W.Y, C. (2017). Patient simulator to drive diagnostic thinking process and deep learning. IJRDO - Journal of Health Sciences and Nursing , 2(7), 45-58. https://doi.org/10.53555/hsn.v2i7.1052