Evaluating Rapid-cycle Deliberate Practice Versus Mastery Learning in Training Nurse Anesthetists on the Universal Anaesthesia Machine Ventilator in Sierra Leone

在塞拉利昂,评估快速循环刻意练习法与精通学习法在培训麻醉护士使用通用麻醉机呼吸机方面的效果

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Abstract

BACKGROUND: Underserved sub-Saharan countries have 0.1 to 1.4 anesthesia providers per 100 000 citizens, below the Lancet Commission's target of 20 per 100 000 needed for safe surgery. Most of these anesthesia providers are nurse anesthetists, with anesthesiologists numbering as few as zero in some nations and 2 per 7 million in others, such as Sierra Leone. In this study, we compared 2 simulation-based techniques for training nurse anesthetists on the Universal Anaesthesia Machine Ventilator-rapid-cycle deliberate practice and mastery learning. METHODS: A 2-week Universal Anaesthesia Machine Ventilator course was administered to 17 participants in Sierra Leone. Seven were randomized to the rapid-cycle deliberate practice group and 10 to the mastery learning group. Participants underwent baseline and posttraining evaluations in 3 scenarios: general anesthesia, intraoperative power failure, and postoperative pulmonary edema. Performance was analyzed based on checklist performance scores and the number of times participants were stopped for a mistake. Statistical significance to 0.05 was determined with the Mann-Whitney U Test. RESULTS: Checklist performance scores did not differ significantly between the 2 groups. When the groups were combined, simulation-based training resulted in a statistically significant improvement in performance. The highest-frequency problem areas were preoxygenation, switching from spontaneous to mechanical ventilation, and executing appropriate treatment interventions for a postoperative emergency. CONCLUSION: Both rapid-cycle deliberate practice and mastery learning are effective methods for simulation-based training to improve nurse anesthetist performance with the Universal Anaesthesia Machine Ventilator in 3 separate scenarios. The data did not indicate any difference between these methods; however, a larger sample size may support or refute our findings.

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