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Foreword

2000· article· en· W4205915551 on OpenAlexaboutno aff
Addeane S. Caelleigh

Bibliographic record

VenueAcademic Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsSnapshot (computer storage)AccreditationReform movementMedical educationPolitical sciencePsychologyPublic relationsMedicineLawComputer science

Abstract

fetched live from OpenAlex

The scale of the Snapshot project was daunting from the beginning. The earliest discussions made clear that the supplement would require unusually complex planning and scheduling. And it would be four times larger than our usual supplements. My early discussions with Brownie Anderson had convinced me, however, that a successful Snapshot supplement would be a major contribution and therefore well worth the effort. Snapshot is an unprecedented collection of reports on curricular structure and change in North American medical education. First, it covers almost all of the accredited medical schools in the United States and in Canada (118 of the 125 in the United States and 12 of the 16 in Canada). Second, the reports have the same basic structure, so that the information can be more readily compared institution to institution. Third, they emphasize process and change rather than quantitative data. Fourth, the information was collected and distilled by a uniquely knowledgeable person. The last time that such a complete picture of U.S. medical education was presented was when Abraham Flexner published the results of his famous survey in 1910. His work is often misunderstood—Flexner did not undertake a dispassionate survey and afterwards make recommendations for reform. Instead, he had a strong agenda of promoting the path of reform already under way at some institutions, and he surveyed U.S. medical schools as a way to highlight the differences between the good schools, the ordinary schools, and the disgracefully bad ones. His report was so successful that the reform movement is associated with him, even though it predated him. Decades later, when the Flexnerian curriculum in its turn needed reform, again the changes were introduced piecemeal, at different speeds, in different surroundings and with widely different outcomes. Despite the commonalities brought about by accreditation and licensure standards, there is great and stimulating variety of educational approaches in our medical schools. However, the variety is difficult to comprehend. This time, unlike 1910, we do not need to know the number of hours that students study particular subjects or whether schools have entrance requirements. Instead, we need to understand the structures of curriculum governance and administration, the approach to curriculum organization, the philosophy of teaching and learning as expressed in the curriculum. The Snapshot collection gives us that. The collection can be used as a reference, to find information about a particular school or about an aspect of the medical curriculum across schools or about an innovation. It gives a general mile marker for how curricula are organized and governed. Most important, it may allow us to create schema, rather than only rank orders, of medical education programs and thus to better understand the structures and systems that can best educate physicians.

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)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.400
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.6000.570

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 head0.027
GPT teacher head0.378
Teacher spread0.351 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2000
Admission routes1
Has abstractyes

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