Face and Content Validation of the LiquidGoldConcept Breast Health Training Tool
Lifelike simulation models are used for training and education of medical professionals. Realistic medical simulators have been used in teaching breast examinations, CPR, intubation, and in birthing simulations. Breast models, specifically, have been used successfully to teach about physical examinations of the breast as it pertains to breast cancer, wide local excisions of breast cancer, and lactation education. However, there is currently no validated breast simulation model that incorporates multiple skin and areolar tones, as well as different types of nipple and areolar shapes and sizes to reflect realistic patients. Furthermore, most breast simulation models are used to teach only one skill, like teaching palpation for a breast mass, and cannot provide learners with the opportunity to practice a comprehensive skill set in breast health. A thorough validation process ensures that the simulator is realistic and can be used to teach the intended skill. Realistic medical simulators can enhance a medical provider’s education, allowing them to better diagnose and treat problems in their patients.
LiquidGoldConcept (LGC) has developed a new product called the Breast Health Training Tool (BHTT) Collection. The collection is comprised of 10 individual silicone breasts and is comprehensive as they depict different benign, malignant, autoimmune, and infectious breast health and lactation illustrations. LGC’s intent when designing the collection was to have realistic breast simulators to facilitate enhanced learning and improve patient outcomes. This product has not yet been validated and, for it to best serve the global community, it needs to be evaluated by experts.
The purpose of this study is to obtain face and content validation of the LGC Breast Health Training Tool Collection. A modified Delphi method, involving a two-round web-based survey amongst physicians will be utilized to reach a consensus about the realism of the BHTTs and the features they depict. The Delphi method uses a panel of experts to obtain an expert consensus on a subject. The data are collected through multiple rounds of structured, anonymous communication until a consensus is reached. An a priori hypothesis has been made that after round one/questionnaire one (Q1), experts will somewhat agree (4/6, intraclass correlation coefficient (ICC) of 0.6) that the features on each BHTT realistically depict a lactation or breast health condition. Based on the results of questionnaire one, a second questionnaire (Q2) will be developed and completed by the expert physician panel. After the second round, the a priori hypothesis is that experts will agree (5/6, and ICC of 0.8) that the features look realistic, granting face and content validation to the LGC BHTT collection.