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Multiuser virtual worlds in healthcare education: A systematic review
Sok Ying Liaw1, Guiller Augustin C Carpio2, Ying Lau1
1Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Background:
The use of multiuser virtual worlds (MUVWs) for collaborative learning has generated interest among healthcare educators. Published evidence to support its use is growing, but none has synthesized the evidence to guide future work.
Objective:
This study sought to provide a comprehensive and systematic evaluation of MUVWs in healthcare education.
Design:
A systematic review METHODS: A systematic search of five databases including CINAHL, Cochrane library, EMBASE, PubMed, and Scopus, was conducted from inception up to January 2017. Two independent researchers selected studies that met the inclusion criteria and assessed for methodological quality using the Medical Education Research Study Quality Instrument (MERSQI). A total of 18 studies were reviewed and their data were synthesized narratively using a 3-P model (presage-process-product).
Results:
Average scores in the MERSQI for methodological quality are 10/18, which is modest. A rally by the government or professional bodies towards more collaborative working among healthcare professionals is a key driver behind implementing MUVWs. Funding is important for its development and evaluation. Team training in acute care and communication training were the most frequent learning objectives, and predominant learning activities include practice on simulation scenario and debriefing. Two-thirds of the studies did not explain their theoretical framework that underpinned their design and implementation of MUVWs. While MUVWs in healthcare education is generally well-received, learning outcomes remain inconclusive.
Conclusion:
Despite a growth of studies on the use of MUVW in healthcare education, there is a need for more understanding of the application of theories to inform the learning activities. Therefore, we suggest educators to incorporate a theoretical model to explain the learning processes behind MUVWs. To improve the quality of evidence, we call for researchers to employ a more rigorous and broader approach to evaluation that explicates longer-term outcomes, including cost benefit analyses.
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