Evidence map›Paper›PMID 32520766›Full record

ArticleSimulation in healthcare : journal of the Society for Simulation in Healthcare2020

Improving the Quality of Evaluation Data in Simulation-Based Healthcare Improvement Projects: A Practitioner's Guide to Choosing and Using Published Measurement Tools.

Chiara M Santomauro, Andrew Hill, Tara McCurdie, Hannah L McGlashan

Abstract read
In one paragraph

Article in Simulation in healthcare : journal of the Society for Simulation in Healthcare, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

4 authors.

Chiara M SantomauroFrom the Clinical Skills Development Service (C.M.S., A.H., T.M., H.L.M.), Metro North Hospital and Health Service; and School of Psychology (C.M.S., A.H., T.M.), The University of Queensland, Brisbane, Queensland, Australia.
Andrew Hill
Tara McCurdie
Hannah L McGlashan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

statementSimulation is increasingly being used in healthcare improvement projects. The aims of such projects can be extremely diverse. Accordingly, the outcomes or participant attributes that need to be measured can vary dramatically from project-to-project and may include a wide range of nontechnical skills, technical skills, and psychological constructs. Consequently, there is a growing need for simulation practitioners to be able to identify suitable measurement tools and incorporate them into their work. This article provides a practical introduction and guide to the key considerations for practitioners when selecting and using such tools. It also offers a substantial selection of example tools, both to illustrate the key considerations in relation to choosing a measure (including reliability and validity) and to serve as a convenient resource for those planning a study. By making well-informed choices, practitioners can improve the quality of the data they collect, and the likelihood that their projects will succeed.

Indexed as

Clinical CompetenceEducational MeasurementHumansOutcome and Process Assessment, Health CareReproducibility of ResultsSimulation Training

Identifiers

PMID32520766
PMCPMC7531509

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.