Mette E Madsen1, Lone N Nørgaard1,2, Ann Tabor1, Lars Konge3, Charlotte Ringsted4, Martin G Tolsgaard2,3. 1. Department of Obstetrics, Juliane Marie Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 2. Department of Gynecology and Obstetrics, Nordsjaellands Hospital, University of Copenhagen, Hillerød, Denmark. 3. Copenhagen Academy for Medical Education and Simulation, University of Copenhagen and The Capital Region of Denmark, Copenhagen, Denmark. 4. Centre for Health Science Education, Faculty of Health, Aarhus University, Aarhus, Denmark.
Abstract
OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation-based training program in transvaginal sonography. The training was conducted on a VR simulator as well as on a physical mannequin. A subgroup of 6 participants underwent subsequent clinical training. During each of the 3 steps, the participants' performance was assessed using instruments with established validity evidence, and they advanced to the next level only after attaining predefined levels of performance. The number of repetitions and time needed to achieve predefined performance levels were recorded along with the performance scores in each setting. Finally, the outcomes were correlated across settings. RESULTS: A good correlation was found between time needed to achieve predefined performance levels on the VR simulator and the physical mannequin (Pearson correlation coefficient .78; P < .001). Performance scores on the VR simulator correlated well to the clinical performance scores (Pearson correlation coefficient .81; P = .049). No significant correlations were found between numbers of attempts needed to reach proficiency across the 3 different settings. A post hoc analysis found that the 50% fastest trainees at reaching proficiency during simulation-based training received higher clinical performance scores compared to trainees with scores placing them among the 50% slowest (P = .025). CONCLUSIONS: Performances during simulation-based sonography training may predict performance in related tasks and subsequent clinical learning curves.
OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation-based training program in transvaginal sonography. The training was conducted on a VR simulator as well as on a physical mannequin. A subgroup of 6 participants underwent subsequent clinical training. During each of the 3 steps, the participants' performance was assessed using instruments with established validity evidence, and they advanced to the next level only after attaining predefined levels of performance. The number of repetitions and time needed to achieve predefined performance levels were recorded along with the performance scores in each setting. Finally, the outcomes were correlated across settings. RESULTS: A good correlation was found between time needed to achieve predefined performance levels on the VR simulator and the physical mannequin (Pearson correlation coefficient .78; P < .001). Performance scores on the VR simulator correlated well to the clinical performance scores (Pearson correlation coefficient .81; P = .049). No significant correlations were found between numbers of attempts needed to reach proficiency across the 3 different settings. A post hoc analysis found that the 50% fastest trainees at reaching proficiency during simulation-based training received higher clinical performance scores compared to trainees with scores placing them among the 50% slowest (P = .025). CONCLUSIONS: Performances during simulation-based sonography training may predict performance in related tasks and subsequent clinical learning curves.