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Record W4285601871 · doi:10.1001/amajethics.2022.556

How an Arts-Based Clinical Skills Set Can Be Assessed During OSCEs

2022· article· en· W4285601871 on OpenAlexaff
Mark Gilbert, Leanne Picketts, Anna MacLeod, Wendy A. Stewart

Bibliographic record

VenueThe AMA Journal of Ethic · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSummative assessmentThe artsSet (abstract data type)Medical educationFormative assessmentAmbiguityPsychologyHealth careMedicineMathematics educationVisual artsComputer scienceArt

Abstract

fetched live from OpenAlex

Background: Arts-based activities' roles in medical education is to challenge students to cultivate clinical skills using ART (aesthetics, reflection, time). ART activities offer opportunities for students to cultivate creative dimensions of their clinical skills and to reflect on their responses to uncertainty and ambiguity. Faculty, however, are challenged to structure these learning activities in diverse, sometimes unfamiliar, health care settings. Methods: This study explored preclerkship medical students' responses to participating in ART activities presented in the common medical educational format of an objective structured clinical exam (OSCE). Activities included interpreting fine art (eg, images and poetry) and drawing a simulated patient. The discussion section transcript and student sketchbooks were analyzed to identify themes related to participating in the study. Results: Use of arts-based activities elicited behaviors similar to those observed in students' responses to formal summative OSCEs, although students also wrestled with challenges and expressed their subjective impressions. Conclusions: This study offers an arts-based tool set capable of being delivered within the familiar medical education setting and established structure of the OSCE.

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)0.006
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
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 integrity0.0000.002
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 head0.124
GPT teacher head0.451
Teacher spread0.327 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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