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Record W2010845727 · doi:10.1353/pbm.2011.0000

Physicianship: Educating for Professionalism in the Post-Flexnarian Era

2011· article· en· W2010845727 on OpenAlexaffabout
J. Donald Boudreau, Sylvia R. Cruess, Richard L. Cruess

Bibliographic record

VenuePerspectives in biology and medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedical educationMedicinePolitical science

Abstract

fetched live from OpenAlex

Although he did not write extensively about professionalism, Abraham Flexner clearly understood its critical role in medical practice. In discerning the basics of medical education he characterized scientific methodology as the instrumental minimum. He left open to future generations the task of defining its necessary complement, the "noble behaviors and fine feelings" required of the medical practitioner. Situated within the current professionalism movement, and informed by previous commentary on the enduring attributes of medicine, a curriculum based on "Physicianship"--the physician as healer and professional--can serve as a logical post-Flexnerian curriculum. The conceptual armature of Physicianship and the attributes necessary for the fulfillment of both the professional and healer role can assist in the selection of students and constitute the educational blueprint for medical teaching. The critically important concepts of identity formation and the requirements for the valid and reliable assessment of professional behaviors of students and faculty are essential components. A Physicianship curriculum, as conceived and deployed at the McGill University Faculty of Medicine, might resonate with Flexner.

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)0.001
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 score0.646
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.074
GPT teacher head0.448
Teacher spread0.374 · 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

Citations38
Published2011
Admission routes2
Has abstractyes

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