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Record W2509969748 · doi:10.7759/cureus.734

Are Educators Actually Coaches? The Implication of Teaching and Learning via Simulation in Education in Healthcare Professions

2016· editorial· en· W2509969748 on OpenAlexaff
W.C. Ian Janes, Dustin Silvey, Adam Dubrowski

Bibliographic record

VenueCureus · 2016
Typeeditorial
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCoachingSet (abstract data type)Experiential learningFunction (biology)PropositionHealth careMedicineMedical educationMathematics educationPsychologyComputer scienceEpistemology

Abstract

fetched live from OpenAlex

Simulation is a unique pedagogical tool designed specifically to develop skills, attitudes, behaviors, and knowledge using experiential learning. Though the teachers in the field of simulation are known as educators, they are generally categorized as educators or coaches and must employ unique pedagogic approaches. Though the aspects of educating and coaching are similar, there are numerous differences that set the two roles apart. Thus, the purpose of this editorial is to highlight the differences between the two roles and also to contextualize their differences, as they relate to simulation in healthcare professions, teaching, and learning. The fundamental proposition of this editorial is to highlight that the teachers who use simulation as their teaching and learning technology function as coaches and not educators as they are currently labeled. Like Haji et al. propose in their article titled "What we call what we do affects how we do it: a new nomenclature for simulation research in medical education," we propose that there needs to be a slight shift in the nomenclature 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.422
Teacher spread0.395 · 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 teacher head, not a consensus.

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".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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