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Enregistrement W2533022215 · doi:10.1097/sih.0000000000000202

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

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

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Notice bibliographique

RevueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2016
Typeletter
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensAlberta Children's HospitalUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésDebriefingComputer scienceObservational studyConsistency (knowledge bases)AssertionPsychological interventionHealth careResearch designInstructional simulationMedical educationPsychologyInstructional designMedicineMathematics educationNursingMultimediaSociologyArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,022
score de la tête « metaresearch » (Gemma)0,016
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,805
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0220,016
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0020,006
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,308
Tête enseignante GPT0,541
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle