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Record W4396892465 · doi:10.1097/acm.0000000000005762

Where Do We Go From Here? An Inventory of Publicly Available Data About Educator Academies, Medical Education Departments, and Offices of Medical Education

2024· article· en· W4396892465 on OpenAlexaboutno aff
Stephanie C. Kerns, Gary L. Beck Dallaghan, Nicole J. Borges, Kathryn N. Huggett

Bibliographic record

VenueAcademic Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationMedical schoolMEDLINEPhysician assistantsMedicinePolitical scienceHealth careNurse practitioners

Abstract

fetched live from OpenAlex

ABSTRACT: With the recent widespread growth and interest among medical educators, analysis of how departments of medical education are structured and their intersection with existing structures within the same institution, such as an office of medical education and/or academy of educators, is warranted. Based on a review of the literature, the authors determined there was a need for an inventory of what medical schools have to offer their faculty, whether it be an office, an academy, or a department. This project sought to inventory the current structures of medical education departments, offices, and academies at U.S. medical schools to explore reporting structure, functions, and characteristics of these entities. Data were extracted from A Snapshot of Medical Student Education in the United States and Canada: Reports From 145 Schools, published in 2020 in the journal Academic Medicine , for each reporting institution. This led to exploration of medical school websites to catalogue institutional structures. Data collected in this inventory demonstrate the range of structures used by medical schools to offer faculty support for their work as teachers and educational researchers. The hypothesis was that departments of medical education would be the least prevalent structures identified in U.S. medical schools, which was indeed a finding. Although the search yielded considerable data for the inventory, there is a dearth of published literature describing current models and characteristics of these different entities. Significant difficulties were encountered locating information clearly delineating roles and responsibilities of each entity on many medical schools' public-facing web pages. Findings are significant because they underscore the challenges medical education leaders have in obtaining information to research, compare, select, and design the administrative model(s) best suited to support faculty educators at their institution. Future work should include creating a detailed catalogue with descriptive information supplied by schools.

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)0.014
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0230.047
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.407
Teacher spread0.357 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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