CONTEMPORARY NURSING EDUCATION STRATEGY

Identify two Studies of a Contemporary Nursing Education Strategy

Identify two Studies of a Contemporary Nursing Education Strategy

Introduction

Simulation is an event or situation made to resemble clinical practice as closely as possible. Simulation can be used to teach theory, assessment, technology, pharmacology, and skills. The emphasis in simulation is often on the application and integration of knowledge, skills, and critical thinking. Unlike a classroom setting or a paper-and-pencil test, simulation allows learners to function in an environment that is as close as possible to an actual clinical situation and provides them an opportunity to think on their feet, not in their seat. Simulation has been successfully used as a teaching strategy in both clinical and formal education. (Issenberg et al., 1992)

Overview

In the context of theoretical inquiry, simulations are tools by which theorists examine the consequences of assumptions. In that respect, it is equivalent to logical analysis, which seeks to derive additional propositions from a set of assumptions. Logical analysis, if possible, is always preferable: Consequences asserted as a result of the outcomes of simulations are open to the criticisms that (1) a slightly different instantiation of the assumptions would have produced different results, (2) the outcomes produced are critically dependent on the initial conditions assumed in the model, and (3) the generalizations proposed hold only for the particular space of parameter values examined. Simulations as theoretical tools are quite distinct from simulation put to other purposes such as training or entertainment (e.g., flight simulators).

As a theoretical tool, simulations are typically used for two reasons. First, a proposed model contains probabilistic elements or nonlinear relations among a large set of variables and the overwhelming complexity of possible outcomes makes it impractical or impossible to derive closed-form solutions of key properties. This use of simulations in these circumstances has a long history in social science; for instance, Rapoport in the 1950s used a deck of cards to simulate a link-tracing process on a biased net, a network composed of ties constructed from random and biased forces (1953). The second use of simulations is somewhat more recent, although it has a precursor in Schelling's famous model of segregation (1969). In this arena, agent-based modelling, the nature of the modelling exercise requires that simulations be used to analyze the model's consequences—the aim is to derive complexity at the aggregate level from the interaction of agents following relatively simple rules at the micro-level. Such complexity is “emergent” relative to the lower-level rules of interaction and agent-state change and thus, in principle, not predictable from these rules. Therefore, simulations must be used to detect such emergent regularities. In such a model, there are typically many agents, and often, probabilistic considerations figure in the determination of who interacts with whom and in the determination of the changes of agent-state change. Logical analysis of such a system is not feasible. The only way to explore consequences is through simulations. Both uses of simulations have been greatly aided by the development of very fast computation easily available on ...