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Teaching the Art of Empathic Interviewing to Third–Year Medical Students using a Fairy Tale—“The Prince Who Turned into a Rooster”

2008· article· en· W2406724792 on OpenAlex
Nancy Joachim

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Psychotherapy · 2008
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsColumbia College
Fundersnot available
KeywordsEmpathyMedical educationGraduate medical educationAccreditationInterviewPsychologyMedical ethicsMedical schoolHumanismCompassionMedicineSocial psychologyLawPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Can empathy be taught? How can we protect the embryonic forms of empathy germinating in our medical students? Can we immunize them against the ravages to their humanism, astutely observed to occur by Henry Silver, Dean of the University of Colorado in 1982, when he published his clear-sighted commentary, Medical Students and Medical School (Silver, 1982; Krugman, 2008). Although studies show that empathy is damaged during medical school, the author proposes that empathic growth through medical school might be possible if enlightened teaching methods are implemented by governing boards, such as the Association of American Medical Colleges (AAMC), The Liaison Committee on Medical Education (LCME), and the Accreditation Council for Graduate Medical Education (ACGME). The author shares a novel teaching method adapted from a technique used by child psychiatrists, storytelling.

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.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.387
Teacher spread0.359 · 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