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Three debriefing methods in virtual patient simulation: A randomized controlled trial
Gul Sahin Karaduman1, Tulay Basak1, Senem Duman1
1University of Health Sciences Turkey, Gulhane Faculty of Nursing, Ankara, Turkey.
Background:
Debriefing is essential after virtual patient simulations; however, optimal approaches remain limited.
Aim:
To evaluate three different debriefing methods used with nursing students following virtual patient simulations, focusing on debriefing experience, simulation effectiveness, satisfaction and self-confidence.
Design:
A blinded, randomized controlled, parallel group with pre- and post-test trial.
Methods:
109 nursing students in their final-year were randomly assigned to three groups by an independent educator: Group A (n = 36), applied the Diamond, Group B (n = 36) applied Debriefing with Meaningful Learning (DML) and the Control Group (n = 37) applied Self-debriefing methods. Participants and the data analyst were blinded. The "Demographic Questionnaire", "Debriefing Experience Scale (DES)", "Simulation Effectiveness Tool-Modified (SET-M)", "Student Satisfaction and Self-confidence in Learning Scale (SCLS)" were used to obtain data.
Results:
Baseline measurements were similar between all groups. The DES showed differences within groups (p < 0.05, d> 0.8) and between groups across all subscales and total scores (p < 0.05). In the SET-M, within-group comparisons revealed significant differences in all groups. In contrast, an important difference was only revealed in the debriefing subscale between groups (p < 0.05). The largest effects in "debriefing" were between Group A versus Control and Group B versus Control (d>0.8). The SCLS revealed significant differences in all groups in within-group comparisons (p < 0.05).
Conclusions:
The results indicate that all debriefing methods are effective regarding DES, SET-M and SCLS scores. DML method yielded the highest gains. Findings offer evidence-based guidance for selecting debriefing strategies to enhance engagement and learning in virtual patient simulations.
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