Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Simulation in Nursing: Historical Analysis and Theoretical Modeling in Support of a Targeted Clinical Training Intervention

Goode, Joseph and Erlen, Judith and O'Donnell, John and Phrampus, Paul and Sereika, Susan (2019) Simulation in Nursing: Historical Analysis and Theoretical Modeling in Support of a Targeted Clinical Training Intervention. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

Download (3MB) | Preview


The use of simulation is widespread in healthcare education, and the potential impact of its use large. This is especially true for nursing education as we look to address problems with obtaining clinical experiences, develop critical thinking skills and create methods to measure the impact of simulation interventions. There is substantial empirical evidence in support of predictive relationships between simulation training interventions and knowledge acquisition. This has been extensively demonstrated with the use of a variety of simulation training modalities from standardized patients to human patient simulators. However, data to support changes in clinical practice and improved patient outcomes are quite limited, including attempts to measure the impact of simulation education on retention and transference of knowledge and skill for more complex healthcare process. Additionally, literature searches reveal that only a handful of authors have engaged in the types of foundational work that any emerging science needs. For example, while pieces of the simulation process have been examined in detail, few have attempted to describe what the process of simulation entails at a macro level. Within the past few years some researchers have begun to ask whether there is a causal or predictive relationship present, but few have explored what these associations may look like structurally and what the evidence for them is. The overall objectives of this current research were to: 1) perform an historical review of simulation in healthcare; 2) use this review to outline a new theoretical model of healthcare simulation; and, 3) conduct a small-scale study aimed at pilot-testing and describing part of that model. Hierarchical Task Analysis (HTA) was used to derive an optimum task set for the standard induction of general anesthesia (OTS-SIGA). New Student Registered Nurse Anesthetists (SRNAs) were trained to this task set, and their adherence to the process steps in the clinical setting was then assessed. We also attempted to measure whether repeating the HTA-derived OTS-SIGA simulation training would have an impact on knowledge and transference of simulation-developed skills to the clinical environment. These measures necessitated the development of associated data collection tools and processes for rater training.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Goode, Josephjsgst33@pitt.edujsgst33
Erlen, Judithjae001@pitt.edujae001
O'Donnell, Johnjod01@pitt.edujod01
Phrampus, Paulphrampus@pitt.eduPhrampus
Sereika, Susanssereika@pitt.eduSSEREIKA
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairErlen, Judithjae001@pitt.edujae001
Committee ChairO'Donnell, Johnjod01@pitt.edujod01
Committee MemberSereika, Susanssereika@pitt.eduSereika
Committee MemberPhrampus, Paulphrampus@pitt.eduphrampus
Date: 24 January 2019
Date Type: Publication
Defense Date: 31 August 2018
Approval Date: 24 January 2019
Submission Date: 14 December 2018
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 151
Institution: University of Pittsburgh
Schools and Programs: School of Nursing > Nursing
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: simulation hierarchical task analysis Theory theoretical modeling induction of anesthesia
Date Deposited: 24 Jan 2019 20:32
Last Modified: 24 Jan 2020 06:15


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item