Evidence map›Paper›PMID 32308988›Full record

ArticleAdvances in simulation (London, England)2020

The use of simulation to prepare and improve responses to infectious disease outbreaks like COVID-19: practical tips and resources from Norway, Denmark, and the UK.

Peter Dieckmann, Kjetil Torgeirsen, Sigrun Anna Qvindesland, Libby Thomas, Verity Bushell, Hege Langli Ersdal

Abstract read
In one paragraph

Article in Advances in simulation (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing 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

60 citing papers in PubMed.

  1. Review
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  7. Metric based virtual simulation training for endovascular thrombectomy improves interventional neuroradiologists' simulator performance.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2023
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  8. Article
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  12. A Newly Developed InterprofessionalJournal of clinical medicine research · 2022
    Article
  13. Article
  14. Simulathon 2020: Integrating Simulation Period Prevalence Methodology Into the COVID-19 Disaster Management Cycle in India.Simulation in healthcare : journal of the Society for Simulation in Healthcare · 2022
    Article
  15. Article
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  19. Airborne Isolation Cardiac Arrest: A Simulation Program for Interdisciplinary Code Blue Team Training.MedEdPORTAL : the journal of teaching and learning resources · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Peter Dieckmann1Department of Quality and Health Technology, Faculty of Health Sciences, University of Stavanger, Stavanger, Norway.
Kjetil Torgeirsen4Stavanger Acute Medicine Foundation for Education and Research (SAFER), Stavanger, Norway.
Sigrun Anna Qvindesland4Stavanger Acute Medicine Foundation for Education and Research (SAFER), Stavanger, Norway.
Libby Thomas6Emergency Department, Kings College Hospital NHS Foundation Trust, London, UK.
Verity Bushell8Postgraduate Medical and Dental Education Department, Kings College London, Denmark Hill, London, UK.
Hege Langli Ersdal1Department of Quality and Health Technology, Faculty of Health Sciences, University of Stavanger, Stavanger, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we describe the potential of simulation to improve hospital responses to the COVID-19 crisis. We provide tools which can be used to analyse the current needs of the situation, explain how simulation can help to improve responses to the crisis, what the key issues are with integrating simulation into organisations, and what to focus on when conducting simulations. We provide an overview of helpful resources and a collection of scenarios and support for centre-based and in situ simulations.

Identifiers

PMID32308988
PMCPMC7160610

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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.