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Record W4200113825 · doi:10.1111/ijsa.12368

Virtual simulation: New method for assessing teamwork skills

2021· article· en· W4200113825 on OpenAlexafffund
Igor Kotlyar, Joe Krasman

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2021
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTeamworkPsychologySituational ethicsApplied psychologyPersonalitySample (material)FidelityPeer assessmentMedical educationSocial psychologyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

Abstract The objective of this study is to explore the potential of a new method for assessing teamwork skills—a virtual high‐fidelity behavioral simulation . In this paper, we describe the development and validation of a chatbot‐driven simulation for assessing teamwork skills of university students. We present the results of a criterion validation study using a sample of 215 undergraduate students. The simulation was found to significantly predict peer ratings following a live team‐based exercise, over and above a teamwork situational judgment test, and a personality inventory. The mean score for women on the simulation was significantly higher than for men. Limitations and future directions are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.504
Teacher spread0.462 · 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.

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

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

Citations15
Published2021
Admission routes2
Has abstractyes

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