Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 267

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Technology-Enhanced Simulation to Assess Health Professionals: A Systematic Review of Validity Evidence, Research Methods, and Reporting Quality

Open access
Cook, David A., Brydges, Ryan, Zendejas, Benjamin, Hamstra, Stanley J., Hatala, Rose·2013·Academic Medicine

Abstract

PURPOSE: To summarize the tool characteristics, sources of validity evidence, methodological quality, and reporting quality for studies of technology-enhanced simulation-based assessments for health professions learners. METHOD: The authors conducted a systematic review, searching MEDLINE, EMBASE, CINAHL, ERIC, PsychINFO, Scopus, key journals, and previous reviews through May 2011. They selected original research in any language evaluating simulation-based assessment of practicing and student physicians, nurses, and other health professionals. Reviewers working in duplicate evaluated validity evidence using Messick's five-source framework; methodological quality using the Medical Education Research Study Quality Instrument and the revised Quality Assessment of Diagnostic Accuracy Studies; and reporting quality using the Standards for Reporting Diagnostic Accuracy and Guidelines for Reporting Reliability and Agreement Studies. RESULTS: Of 417 studies, 350 (84%) involved physicians at some stage in training. Most focused on procedural skills, including minimally invasive surgery (N=142), open surgery (81), and endoscopy (67). Common elements of validity evidence included relations with trainee experience (N=306), content (142), relations with other measures (128), and interrater reliability (124). Of the 217 studies reporting more than one element of evidence, most were judged as having high or unclear risk of bias due to selective sampling (N=192) or test procedures (132). Only 64% proposed a plan for interpreting the evidence to be presented (validity argument). CONCLUSIONS: Validity evidence for simulation-based assessments is sparse and is concentrated within specific specialties, tools, and sources of validity evidence. The methodological and reporting quality of assessment studies leaves much room for improvement.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/acm.0b013e31828ffdcf

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 267

Metadata

Authors
Cook, D. A., Brydges, R., Zendejas, B., Hamstra, S. J., & Hatala, R.
Venue
Academic Medicine
Published
2013-06
Volume
88
Issue
6
Pages
872-883
ISSN
1040-2446 (ISSN-L)
OpenAlex
W2330566212
Cited by
267
References
4
FWCI
13.47
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.