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Record W3138621382 · doi:10.33425/2639-9474.1152

A Scoping Review and Analysis of Simulation Facilitator Essential Elements

2020· review· en· W3138621382 on OpenAlexfundno aff
L Hardie, Lori Lioce

Bibliographic record

VenueNursing & Primary Care · 2020
Typereview
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsnot available
FundersYork UniversityUniversity of Connecticut
KeywordsFacilitatorComputer sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

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..

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)0.029
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.100
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0280.022
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.027
GPT teacher head0.376
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 designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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