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Article
Publication date: 15 February 2013

Annette T. Maruca and Desiree A. Diaz

Alcohol withdrawal syndrome (AWS) is a serious condition that can result in death if it is not recognized and treated appropriately. A high fidelity simulation scenario on AWS was…

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Abstract

Purpose

Alcohol withdrawal syndrome (AWS) is a serious condition that can result in death if it is not recognized and treated appropriately. A high fidelity simulation scenario on AWS was created for psychiatric nurses in an educational setting that focused on the recognition of alcohol withdrawal, implementation of the Clinical Institute Withdrawal Assessment (CIWA) and initiation of appropriate treatment and management of AWS.

Design/methodology/approach

A formative assessment teaching strategy was used to evaluate the development and implementation of a high fidelity simulation (HFS) on alcohol withdrawal syndrome. The HFS was part of a baccalaureate undergraduate psychiatric nursing education designed to reinforce classroom theory and clinical application. Results were measured using a self‐report survey completed by students that focused on the details of content and performance.

Findings

Feedback from the 38 nursing students who evaluated the HFS was overall positive. Survey results showed that the HFS scenario reinforced the classroom theory on addiction and mental disorders while translating and supporting student's learning to clinical practice. The HFS provided opportunity for students to practice skills when they had not had this experience during the clinical rotation. Only four students felt uncomfortable with decision making and initiating the CIWA scale after the HFS.

Originality/value

There is a gap in the development and use of standardized simulations for mental health and substance abuse scenarios as a teaching strategy in nursing programs. The results of this study supported using HFS as an educational strategy and set the stage for future complex simulations such as dual diagnosis and clients with comorbidities.

Details

Advances in Dual Diagnosis, vol. 6 no. 1
Type: Research Article
ISSN: 1757-0972

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