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Record W2168501529 · doi:10.1093/jhmas/jru026

Making the Case for History in Medical Education: Fig. 1.

2014· article· en· W2168501529 on OpenAlexaff
David S. Jones, Jeremy A. Greene, Jacalyn Duffin, John Harley Warner

Bibliographic record

VenueJournal of the History of Medicine and Allied Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsSkepticismReductionismContingencyValue (mathematics)Perspective (graphical)PoliticsMedical historyHistory of medicineMedical educationMedicinePsychologyEpistemologyLawPolitical sciencePhilosophyComputer sciencePathologySurgery

Abstract

fetched live from OpenAlex

Historians of medicine have struggled for centuries to make the case for history in medical education. They have developed many arguments about the value of historical perspective, but their efforts have faced persistent obstacles, from limited resources to curricular time constraints and skepticism about whether history actually is essential for physicians. Recent proposals have suggested that history should ally itself with the other medical humanities and make the case that together they can foster medical professionalism. We articulate a different approach and make the case for history as an essential component of medical knowledge, reasoning, and practice. History offers essential insights about the causes of disease (e.g., the non-reductionistic mechanisms needed to account for changes in the burden of disease over time), the nature of efficacy (e.g., why doctors think that their treatments work, and how have their assessments changed over time), and the contingency of medical knowledge and practice amid the social, economic, and political contexts of medicine. These are all things that physicians must know in order to be effective diagnosticians and caregivers, just as they must learn anatomy or pathophysiology. The specific arguments we make can be fit, as needed, into the prevailing language of competencies in medical education.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.083
GPT teacher head0.377
Teacher spread0.294 · 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 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

Citations58
Published2014
Admission routes1
Has abstractyes

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