While enhancing perioperative nursing competency is crucial for improving surgical quality and patient safety, conventional apprenticeship-based training models often fall short in addressing the systematic clinical training needs of junior circulating nurses. This study aims to bridge this gap by innovatively integrating scenario-based simulation with the structured BOPPPS teaching model to enhance training efficacy and clinical performance. The aim of this study was to evaluate the effectiveness of integrating the BOPPPS (bridge-in, objective, pre-assessment, participatory learning, post-assessment, summary) teaching model with scenario-based simulation in the training of junior nurses in operating room circulation roles. This study employed a quasi-experimental design utilizing a pre-post self-controlled approach.From September to October 2023, 20 junior circulating nurses from a single hospital were randomly assigned to five groups. A structured teaching framework based on the BOPPPS model was implemented, with scenario-based simulation incorporated into the participatory learning component to support training in circulation responsibilities. Each group was randomly assigned one clinical case for simulation. Pre- and post-training assessments were conducted to evaluate the quality of circulation work, work execution rate, incidence of adverse events, and training satisfaction. Following the training, the quality of circulating work significantly increased from 71.6 ± 6.37 points to 85.05 ± 3.63 points (t = 30.19, p < .05), and the work completion rate improved from 69.95% to 82.55% (t = 30.20, p < .05). Additionally, the incidence of adverse events decreased markedly, and the overall satisfaction of nurses with the training program reached 96.11%. This study integrated the BOPPPS teaching model with scenario simulation training, resulting in a significant improvement of 13.45 points (p < .05) in the quality of circulating work among junior operating room nurses, along with a 12.6% increase in task completion rate. It is recommended to further optimize the training process by refining the interpretation of learning objectives, dynamically adjusting the difficulty of simulation cases, incorporating self-assessment of job competency, and tracking medium- to long-term outcomes. Additionally, the ADDIE model could be applied to support continuous improvement of the training system.
Mu et al. (Mon,) studied this question.