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Record W2169252860 · doi:10.1177/0969733010386163

Moral imagination in simulation-based communication skills training

2011· article· en· W2169252860 on OpenAlexaff
Ruth P. Chen

Bibliographic record

VenueNursing Ethics · 2011
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConceptualizationPsychologyImaginationCommunication skillsMoral educationPedagogyComputer scienceMedical educationMedicineCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Clinical simulation is used in nursing education and in other health professional programs to prepare students for future clinical practice. Simulation can be used to teach students communication skills and how to deliver bad news to patients and families. However, skilled communication in clinical practice requires students to move beyond simply learning superficial communication techniques and behaviors. This article presents an unexplored concept in the simulation literature: the exercise of moral imagination by the health professional student. Drawing from the works of Hume, Aristotle and Gadamer, a conceptualization of moral imagination is first provided. Next, this article argues that students must exercise moral imagination on two levels: towards the direct communication exchange before them; and to the representative nature of simulation encounters. Last, the limits of moral imagination in simulation-based education 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 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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.008
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.343
GPT teacher head0.493
Teacher spread0.150 · 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 designQualitative
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

Citations35
Published2011
Admission routes1
Has abstractyes

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