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Record W2315916874 · doi:10.1097/acm.0b013e3181f5ced4

Commentary: Are We Ready to Embrace the Rest of the Flexner Report?

2010· article· en· W2315916874 on OpenAlexaboutno aff
Garrett Riggs

Bibliographic record

VenueAcademic Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsRest (music)MEDLINEPsychologyMedicineMedical educationPolitical scienceInternal medicineLaw

Abstract

fetched live from OpenAlex

At the start of the 20th century, Abraham Flexner proposed a number of reforms for medical education in his seminal 1910 report, Medical Education in the United States and Canada. His recommendations were wide ranging, including a strong scientific basis, use of pedagogical methods, and faculty whose principal role is that of educator. Of these, reforms in science education for medicine received the widest attention and revolutionized physicians' intellectual foundations for medical practice. But what of Flexner's other suggested reforms, those skills needed to develop "the educated man" who can meet the "greatly modified ethical responsibility" of modern medical practice? As the 21st century begins, Flexner's ideas on these other subjects he considered critical for physician training are reappearing in the medical education literature. If history is a guide, medical education could be on the cusp of another set of great advances by renewing interest in medical humanities, reevaluating the makeup of medical school teaching faculty, and seeking innovations in pedagogy to facilitate active and integrated learning. The time is ripe to embrace the rest of the Flexner Report.

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)0.011
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.081
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.099
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0060.009
Scholarly communication0.0050.010
Open science0.0090.003
Research integrity0.0810.075
Insufficient payload (model declined to judge)0.0100.009

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 head0.042
GPT teacher head0.394
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations19
Published2010
Admission routes1
Has abstractyes

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