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Record W2566391158

It was like a mirror : a reflection on filmed role play simulation

2015· article· en· W2566391158 on OpenAlexaboutno aff
Karen Dodge

Bibliographic record

VenueMount Royal University Institutional Repository (Mount Royal University) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsReflection (computer programming)Computer scienceComputer graphics (images)
DOInot available

Abstract

fetched live from OpenAlex

“What brings you here today?” is a familiar question in a health clinic in Canada, but it may not be one that comes immediately to the mind of an internationally educated health professional. The way health professionals communicate with patients in their cultures can sound overly direct in Canadian clinics. “Why are you here?” would be typically asked to patients in settings such as the Ukraine and Egypt. A stepping stone that supports the understanding of linguistic appropriacy and the Canadian health care context is offered at the Languages Institute of Mount Royal University, Calgary. The Communication Skills for Health Professionals (CSHP) project teaches language and communication skills through a performance based approach. Our scholarship of teaching and learning inquiry explored how students in this project value filmed role play simulations as a learning tool for developing communication skills and knowledge. This article describes the instructional context of our study, its methodology, four key findings and implications for the role of the English language instructor, for student learning and for program implementation. The impact of filmed role play simulation on learning, acculturation into the Canadian health care context, professional identity formation, and the integration of communication skills within various contexts 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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.063
GPT teacher head0.337
Teacher spread0.274 · 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 designNot applicable
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

Citations3
Published2015
Admission routes1
Has abstractyes

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