Development of Distance Simulation Educator Guidelines in Healthcare
Bibliographic record
Abstract
INTRODUCTION: The abrupt disruption of in-person instruction in health care during the COVID-19 pandemic resulted in the rapid adoption of distance simulation as an immediate alternative to providing in-person simulation-based education. This massive instructional shift, combined with the lack of educator training in this domain, led to challenges for both learners and educators. This study aimed to disseminate the first set of competencies required of and unique to effective distance simulation educators. METHODS: This was a multiphasic and iterative modified Delphi study validating the content of carefully and rigorously synthesized literature. Experts were invited from around the globe to participate in this study with mandatory attendance at an annual health care simulation conference to openly discuss the guidelines presented as competencies in this document. We divided each competency into "Basic" and "Advanced" levels, and agreement was sought for these levels individually. The experts provided their opinion by choosing the options of "Keep, Modify, or Delete." A free-marginal kappa of 0.60 was chosen a priori. RESULTS: At the conclusion of the Delphi process, the number of competencies changed from 66 to 59, basic subcompetencies from 216 to 196, and advanced subcompetencies from 179 to 182. CONCLUSIONS: This article provides the first set of consensus guidelines to distance simulation educators in health care, and paved the way for further research in distance simulation as a modality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".