Emerging Discourses Shaping High-Fidelity Simulation as an Education Platform in Aotearoa New Zealand Pre-registration Nursing Education: A Foucauldian Discourse Analysis

Date
2022
Authors
Bowen-Withington, Julie Nicola
Supervisor
Neville, Stephen
Zambas, Shelaine
Cook, Catherine
Item type
Thesis
Degree name
Doctor of Philosophy
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Publisher
Auckland University of Technology
Abstract

There is much literature supporting the use of high-fidelity simulation as a teaching modality in healthcare education. Few, however, offer a critical reading of simulated learning to examine the discourses and discursive practices shaping undergraduate nurses’ experiences and knowledge, or how simulated learning translates into practice. The emergence of high-fidelity simulation as a teaching-learning modality raises the question of centrality of power as to what discourses influence the use of high-fidelity simulation and ultimately learning.

This study critically examines the emerging discourses and discursive practices that influence the use of high-fidelity simulation in pre-registration nursing education in Aotearoa New Zealand. Data for this research were located from journal articles and interviews with academic nurse educators, Bachelor of Nursing students, and others. Discourse analysis informed by the work of Michel Foucault and others revealed powerful nursing and medical discourses, and the existence of sub-discourses of simulation pedagogy, realism and replacement of actual clinical hours. In this study, I have focused on the disciplinary technologies and the effect these technologies have on constructing professional subjectivities of nursing students and academic nurse educators experiencing high-fidelity simulation. The emergence of practices which establish and resist such discourses have made it easier to briefly glimpse into the conditions that made high-fidelity simulation as a teaching modality possible, whilst also creating the possibility for thinking about high-fidelity simulation and simulation technology in a different light.

Analysis reveals that utilising the technology involved in high-fidelity simulation has the potential to provide an immersive clinical learning experience that, as the scholarly literature attests, fits the preferences of the millennial student. However, the pre-registration nursing student interviewees in this study did not conform to this millennial student discourse, instead preferring actual clinical experiences with patients, to learn how to be a nurse and learn how to establish therapeutic relationships with the patient, including family and whānau. Alternative discourse emerging in literature, narratives of academic nurse educators and pre-registration students, talks of pre-registration nursing education valuing actual clinical experiences as an essential element for learning nursing practice. Conversely proponents of HFS contest that well developed and structured simulation activities, including debriefing, can provide a platform to apply theory to practice, albeit simulated practice. What is clear is that with the proliferation of technology in nursing education, additional research is required to establish a more substantial evidence base to inform future curricula and policy development, particularly regarding the continuing debate of HFS replacing clinical experiences.

The contribution this thesis makes is to highlight the contested domain of high-fidelity simulation as a teaching modality in pre-registration nursing education. By acknowledging such discourses and discursive practices within which we work as academic nurse educators, we may recognise more clearly the processes by which we select what is crucial, and what is not. Further research that connects nursing student learning utilising high-fidelity simulation, including the student experience would significantly improve understanding of the impact of high-fidelity simulation on student learning and transfer of that learning to their new graduate experience.

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Keywords
Pre-registration , Nursing , Education , High-fidelity , Simulation
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