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1.
J Clin Nurs ; 2021 Aug 27.
Article in English | MEDLINE | ID: covidwho-2301098

ABSTRACT

AIM: The aim of this review was to synthesise current knowledge of high-fidelity simulation practices and its impact on nurse clinical competence in the acute care setting. BACKGROUND: There is no consensus or standardisation surrounding best practices for the delivery of high-fidelity simulation in the acute care setting. This is an understudied area. DESIGN: An integrative review using Johns Hopkins Nursing Evidence-Based Practice Model. METHODS: Medical subject heading terms 'Clinical Competence', AND 'High Fidelity Simulation Training', AND 'Clinical Deterioration' were systematically searched in PubMed, CINAHL and Embase databases for peer-reviewed literature published through September 2020. The current study was evaluated using PRISMA checklist. RESULTS: Seven studies met the inclusion criteria. Three main concepts were identified: modes of delivery, approach to learner participation and outcome measurement. CONCLUSIONS: This review substantiated the use of high-fidelity simulation to improve acute care nurses' early identification and management of clinical deterioration. Global variations in course design and implementation highlight the need for future approaches to be standardised at the regional level (i.e., country-centric approach) where differing scopes of practice and sociocultural complexities are best contextualised. RELEVANCE TO CLINICAL PRACTICE: These findings add to the growing body of evidence of simulation science. Important considerations in course planning and design for nursing clinical educators were uncovered. This is especially relevant given the current COVID-19 pandemic and urgent need to train redeployed nurses safely and effectively from other units and specialties to acute care.

2.
Stud Health Technol Inform ; 290: 479-483, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1933565

ABSTRACT

The global COVID-19 pandemic has driven innovations in methods to sustain initiatives for the design, development, evaluation, and implementation of clinical support technology in long-term care settings while removing risk of infection for residents, family members, health care workers, researchers and technical professionals. We adapted traditional design and evaluation methodology for a mobile clinical decision support app - designated Mobile Application Information System for Integrated Evidence ("MAISIE") - to a completely digital design methodology that removes in-person contacts between the research team, developer, and nursing home staff and residents. We have successfully maintained project continuity for MAISIE app development with only minor challenges while working remotely. This digital design methodology can be implemented in projects where software can be installed without in-person technical support and remote work is feasible. Team skills, experience, and relationships are key considerations for adapting to digital environments and maintaining project momentum.


Subject(s)
COVID-19 , Decision Support Systems, Clinical , Mobile Applications , Health Personnel , Humans , Long-Term Care , Pandemics
3.
J Nurses Prof Dev ; 37(4): 206-210, 2021.
Article in English | MEDLINE | ID: covidwho-1189532

ABSTRACT

The COVID-19 pandemic emphasized the importance of preparing nursing staff at healthcare organizations to adequately respond and care for the influx of patients infected with the virus. Training redeployed nursing staff on equipment basics of acute care nursing while following social distancing guidelines posed a challenge. A skills practice laboratory was implemented utilizing a self-learning methodology while adhering to social distancing guidelines. This had favorable results in meeting objectives and improving anxiety and confidence.


Subject(s)
COVID-19 , Nursing Staff, Hospital/standards , Physical Distancing , Simulation Training , Hospitals , Humans , Organizational Innovation
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