Evidence map›Paper›PMID 34721739›Full record

ArticleClinical simulation in nursing2022

In-Situ Simulation for Intensive Care Nurses During the COVID-19 Pandemic in Italy: Advantages and Challenges.

Alessandro Monesi, Guglielmo Imbriaco, Carlo Alberto Mazzoli, Aimone Giugni, Patrizia Ferrari

Abstract read
In one paragraph

Article in Clinical simulation in nursing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Trial
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alessandro MonesiIntensive Care Unit, Maggiore Hospital, Bologna, 40133, Italy.
Guglielmo ImbriacoCritical Care Nursing Master course, University of Bologna, Bologna, Italy.
Carlo Alberto MazzoliDivision of Anesthesia, Intensive Care, and Prehospital Emergency, Maggiore Hospital, Bologna, 40133, Italy.
Aimone GiugniDivision of Anesthesia, Intensive Care, and Prehospital Emergency, Maggiore Hospital, Bologna, 40133, Italy.
Patrizia FerrariIntensive Care Unit, Maggiore Hospital, Bologna, 40133, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic required a global increase in intensive care unit (ICU) resources and the recruitment of a great number of nurses without any tprior critical care experience. The opportunities for traditional education and supervised clinical training were limited to prevent infections. The massive increase of nursing staff resources required a rethinking of the usual educational strategies for newly acquired nurses. This short communication describes our experience of an "in-situ" simulation training course in an Italian tertiary level hospital. A series of two-part classes were structured with short lectures on fundamental principles of intensive care nursing and brief hands-on sessions, and a set of simulated scenarios, based upon the most common situations to be faced in the ICU. In-situ simulation offers greater realism and transferability and represents a cost-effective strategy, avoiding the costs and the maintenance of a dedicated simulation center. The simulated multidisciplinary teamwork in the real ICU setting contributes to an effective experiential learning, improving staff familiarity with devices, equipment, and environment, and allows trainees to improve both technical and nontechnical skills.

Indexed as

CCN, critical care nursesCOVID-19critical care nursingICU, intensive care unitin-situ simulationintensive care unitNTS, non-technical skillsPPE, personal protective equipmentsimulation-based trainingstaffing

Identifiers

PMID34721739
PMCPMC8542439

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.