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Enregistrement W4414049632 · doi:10.54531/vysb7165

A45 Simulation Facilitator Survey Results from a Pan-Canadian Virtual Simulation Program

2024· article· en· W4414049632 sur OpenAlexaboutno aff
Sandra Goldsworthy, Margaret Verkuyl

Notice bibliographique

RevueJournal of Healthcare Simulation · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFacilitatorThematic analysisFocus groupPerceptionModalitiesProcess (computing)Exploratory researchQualitative research

Résumé

récupéré en direct d'OpenAlex

Introduction: While much is known about students’ experiences and outcomes with virtual simulation (VS), little is known about the skills required to conduct the complex activity of facilitating simulation in the virtual environment [1], nor the needs and experiences of facilitators.1A successful experience goes far beyond simply offering learners’ access to a VS; it requires a facilitator who understands the learners’ needs and course objectives, can create a welcoming virtual space that promotes learning, and can evaluate the experience. Currently, there is a gap in our understanding of the best ways to facilitate the different modalities used in VS and what skills, professional development, experience, and supports facilitators need. Research Questions: 1) How well prepared were facilitators in the Virtu-WIL project, i.e., what were the facilitators’ perceptions of their training needs and what recommendations did they have for training? 2) From a student and a facilitator perspective, what was the impact of the VS on` student learning? 3) What impact did the VS have on students’ readiness for the clinical setting/workplace and what factors contributed to that impact? Methods: An exploratory qualitative research process was conducted to explore simulation facilitators’ experiences with the virtual simulations using individual interviews. In addition, we used focus groups to assess the impact on students. A facilitator or student interview guide was used by the researchers. Data were analysed by the authors using a thematic content analysis [2]. Results: Ten facilitators from six educational institutions participated in the study: three from nursing, three from medical laboratory technology and four from paramedicine. Twenty-one students from five institutions participated: 8 from paramedicine and 13 from nursing. Some facilitators had previous simulation training and experience while others had no prior simulation experience. Two major themes were identified: The Facilitator Experience and VS: Impact on Learning. Facilitators and students were clear: to be effective, VSs need to align with course learning objectives, meet learner needs, and be skilfully facilitated. Effective facilitation had a positive impact on student outcomes. Discussion: We learned the importance of a skilled facilitator in all stages of simulation pedagogy. The facilitator plays a vital role and it is not sufficient to be trained in in-person simulation, facilitators need training in the nuances of VS. Our study highlights the complexity of the facilitator role in which they have to use their knowledge and skills to create a safe, stimulating learning environment to enhance the learning environment. Ethics statement: Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable. References 1. Hodges B, Albert M, Arweiler D, et al. The future of medical education: A Canadian environmental scan. Medical Education. 2011;45:95–106. 2. Leigh E, Likhacheva E, Tipton E, de Wijse-van Heeswijk M, Zürn B. Why facilitation? Simulation & Gaming. 2021;52(3):247–254. Acknowledgments: This project was funded by Colleges and Institutes of Canada, Government of Canada.

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.

Comment cette classification a été obtenuedéplier

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)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,280
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,094
Tête enseignante GPT0,441
Écart entre enseignants0,348 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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