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Record W4414049632 · doi:10.54531/vysb7165

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

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

Bibliographic record

VenueJournal of Healthcare Simulation · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorThematic analysisFocus groupPerceptionModalitiesProcess (computing)Exploratory researchQualitative research

Abstract

fetched live from 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.

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)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.575

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.094
GPT teacher head0.441
Teacher spread0.348 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
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