A Scoping Review and Analysis of Simulation Facilitator Essential Elements
Bibliographic record
Abstract
Background: Quality healthcare simulation depends largely on skilled facilitation.Identification and development of essential facilitator competencies challenges healthcare simulation operations across the world.Many organizations and authors have articulated and developed standards or recommendations for simulation facilitator development, but none have provided a synthesized and operationalized list of simulation facilitator competencies.Method: Analysis of organizational standards and recommendations for simulation, combined with a scoping review of recent literature, provide a synthesis of essential elements required for simulation facilitators.Eight documents from six professional organizations, and 23 literature articles identified from the scoping review, were analyzed for statements related to facilitator competencies and/or essential elements, which were then coded, and themes abstracted using a computer assisted qualitative data analysis software.Elements were then expanded and operationalized.Results: From the eight documents and 23 articles, content analysis identified over 1200 facilitator competency related statements which were thematically grouped into seven categories: 1) Knowledge underpinning simulation, 2) Skills to deliver simulation, 3) Skills to support participants, 4) Skills to support debriefing and/or assessment, 5) Facilitator comportment or qualities, 6) Commitment to continuous quality and safety improvement, and 7) Committment to professional development at every level.The analysis identified 30 essential elements which were expanded and operationalized into 149 sub-elements for evidence-based facilitation.Conclusion: Simulation facilitator development should be based on expert consensus and evidence-based practice.This research expands and operationalizes essential elements of healthcare simulation facilitation and provides a focused pathway for development and professional advocacy to advance the science of simulation..
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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 | 0.029 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".