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Record W2533022215 · doi:10.1097/sih.0000000000000202

Highlighting Instructional Design Features in Reporting Guidelines for Health Care Simulation Research

2016· letter· en· W2533022215 on OpenAlexaffabout
Adam Cheng, Vinay Nadkarni, Todd P. Chang, Marc Auerbach

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2016
Typeletter
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsDebriefingComputer scienceObservational studyConsistency (knowledge bases)AssertionPsychological interventionHealth careResearch designInstructional simulationMedical educationPsychologyInstructional designMedicineMathematics educationNursingMultimediaSociologyArtificial intelligence

Abstract

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Dear Editor In a commentary entitled “Reporting Guidelines for Health Care Simulation Research: Where is the Learning”,1 Dr. E. Salas correctly highlights the vital importance of describing the instructional features of learning when publishing simulation-based educational research. He succinctly lists a number of critically important elements of simulation-based instructional design, including: feedback and debriefing, learning objectives, scenario development (and associated triggers), and performance assessment. Dr. Salas expresses concern that the recently published reporting guidelines for health care simulation research2–5 do not adequately emphasize or include these key elements of simulation research. We agree with Dr. Salas and his assertion that instructional design features of simulation-based educational interventions are of paramount importance. Of note, we previously championed this concept in a separate manuscript on simulation-based research.6 The project to develop reporting guidelines for health care simulation research involved designing extensions to both the Consolidated Standards of Reporting Trials (CONSORT)7 and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)8 Statements. In an effort to maintain consistency in form and function with previously published versions of the CONSORT and STROBE statements and checklists, we elected not to add new items to each list, but rather build on existing items with simulation-specific extensions. We agree with Dr. Salas’s commentary that “a fundamental aspect of simulation-based research is to determine what works best for training purposes.”1 To emphasize the importance of reporting specific instructional design features of the simulation-based educational intervention, we published a table featuring Key Elements to Report for Simulation-based Research (Table 3) in the reporting guidelines manuscripts.2–5 This table serves as an additional checklist to report items specific to simulation-based research, and is meant to supplement item 5 (interventions) on the CONSORT Statement and item 7 (variables) on the STROBE Statement. Key elements include the following: (1) participant orientation, (2) simulation type, (3) simulation environment, (4) simulation event/scenario, (5) instructional design or exposure and (6) feedback and/or debriefing. Each key element has associated sub-elements and descriptors that can be used to help investigators design and report simulation-based research. The importance of outcomes and methods of assessment are highlighted with new simulation-specific extensions for item 6 (outcomes) of the CONSORT Statement and item 8 (data sources/measurement) of the STROBE Statement. Thus, we believe that critical elements of instructional design are indeed incorporated into the current CONSORT and STROBE statements for health care simulation research.2–5 For illustration, the element “feedback and/or debriefing” has 9 sub-elements: source, duration, facilitator presence, facilitator characteristics, content, structure/method, timing, video and scripting. A study describing the impact of a debriefing intervention should describe each of these sub-elements in detail, and make note if these sub-elements were not used (eg. debriefing script) in the study. Some elements and/or sub-elements may not be applicable to all studies. For further detail, we refer readers to the explanation and elaboration document published as part of the reporting guidelines, found at https://links.lww.com/SIH/A266 (Explanation and Elaboration of the Simulation-Specific Extensions for the CONSORT and STROBE Statements). This document provides illustrative examples for how to report items with new extensions, including item 5 (interventions) of the CONSORT Statement and item 7 (variables) of the STROBE Statement. Simulation-based research scientists are encouraged to be thorough but appropriately selective when reporting their educational interventions using this table. Whereas the table of key elements is not part of either the CONSORT or STROBE checklists, we encourage authors, reviewers, and editors to use the table as a separate checklist when writing or reviewing papers describing simulation-based educational research. We invite the simulation research community to provide feedback on these elements at http://inspiresim.com/simreporting/so that we can work toward modification of the reporting guidelines in the future. As Dr. Salas states, these instructional design features “are critical to improve the science of simulation”.1 Sincerely yours, Adam Cheng, MD, FRCPC University of Calgary KidSim-ASPIRE Research Program Division of Emergency Medicine Department of Pediatrics Alberta Children’s Hospital 2888 Shaganappi Trail NW, Calgary Alberta, Canada [email protected]Vinay M. Nadkarni, MD Department of Pediatrics Children’s Hospital of Philadelphia University of Pennsylvania Perelman School of MedicineTodd P. Chang, MD Department of Pediatrics Children’s Hospital of Los Angeles University of Southern CaliforniaMarc Auerbach, MD, MSc Department of Pediatrics Section of Emergency Medicine Yale University School of Medicine

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.709
metaresearch head score (Gemma)0.915
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.291
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7090.915
Meta-epidemiology (narrow)0.0040.007
Meta-epidemiology (broad)0.0090.012
Bibliometrics0.0190.020
Science and technology studies0.0060.023
Scholarly communication0.0220.024
Open science0.0180.016
Research integrity0.0410.049
Insufficient payload (model declined to judge)0.0060.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.308
GPT teacher head0.541
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2016
Admission routes2
Has abstractyes

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