Evidence map›Paper›PMID 35309206›Full record

ReviewFrontiers in public health2022

Trends and Innovations of Simulation for Twenty First Century Medical Education.

Eduardo Herrera-Aliaga, Lisbell D Estrada

Abstract readReview
In one paragraph

Review in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 2 pooled it
–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

45 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

2 authors.

Eduardo Herrera-AliagaFaculty of Health Sciences, Universidad Bernardo O'Higgins, Santiago, Chile.
Lisbell D EstradaFaculty of Health Sciences, Universidad Bernardo O'Higgins, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last two decades there has been an enormous growth in the use of clinical simulation. This teaching-learning methodology is currently the main tool used in the training of healthcare professionals. Clinical simulation is in tune with new paradigms in education and is consistent with educational theories that support the use of experiential learning. It promotes the development of psychomotor skills and strengthens executive functions. This pedagogical approach can be applied in many healthcare topics and is particularly relevant in the context of restricted access to clinical settings. This is particularly relevant considering the current crisis caused by the COVID-19 pandemic, or when trying to reduce the frequency of accidents attributed to errors in clinical practice. This mini-review provides an overview of the current literature on healthcare simulation methods, as well as prospects for education and public health benefits. A literature search was conducted in order to find the most current trends and state of the art in medical education simulation. Presently, there are many areas of application for this methodology and new areas are constantly being explored. It is concluded that medical education simulation has a solid theoretical basis and wide application in the training of health professionals at present. In addition, it is consolidated as an unavoidable methodology both in undergraduate curricula and in continuing medical education. A promising scenario for medical education simulation is envisaged in the future, hand in hand with the development of technological advances.

Indexed as

COVID-19Education, MedicalComputer SimulationCurriculumHumansPandemicscompetencesCOVID-19healthcaremedical educationpublic healthsimulation

Identifiers

PMID35309206
PMCPMC8929194

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.