| Summary |
About one third of the more than 700,000 deaths that occur in hospitals each year are unanticipated. Without appropriate training and experience, a patient's impending death can be overwhelming, and an unexpected death can be even more confusing and traumatic for the novice nurse. Nurse educators have incorporated simulation activities to increase pre-licensure nursing students' competencies in end-of-life and palliative care. However, these types of simulations do not help prepare students to care for patients that die unexpectedly. Due to the perceived negative effects that stress and anxiety could have on pre-licensure students, simulation professionals remain reluctant to expose students to unexpected death simulations. Prior research indicated that students experience large amounts of stress and anxiety in critical care simulations, but explanations are lacking with regards to the variance in students' performance in meeting simulation outcomes; some students perform well, while others do not. Furthermore, the minimal research found related specifically to unexpected death simulations did not explore the relationships between stress, anxiety, learning outcomes and potential moderating factors such as resilience, and was found to lack methodological and statistical rigor. Therefore, it is prudent to explore the effects of stress, anxiety, and resilience, and students' perceptions of an unexpected death simulation. A descriptive, correlational, mixed methods design using a convergent, parallel QUAN+QUAL technique was used as the research method for this study. A pilot study informed and helped finalized methods and procedures. In addition to the research protocol, safety protocols related to COVID-19 were finalized and Institutional Review Board permission was obtained. A convenience sample of students was recruited from a small eastern North Carolina community college where the fourth semester of an Associated Degree Nursing program includes a stroke simulation which leads to unexpected death. Study data were collected by research assistants and the principal investigator, who then prepared the data for analysis. Analytical methods included descriptive statistics, statistical procedures to explore relationships among variables and to compare groups, and one-on-one interviews; these were then placed in a meta-matrix for a combined analysis of QUANT+QUAL data. Study results from the pilot study are discussed in chapter four, while the results of the dissertation study are discussed in chapter five. |
