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Record W3174495609 · doi:10.4103/amhs.amhs_111_21

Defining Medical Education Scholarship

2021· article· en· W3174495609 on OpenAlexaff
ElizabethM Wooster, DouglasL Wooster, JerryM Maniate

Bibliographic record

VenueArchives of Medicine and Health Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsOttawa HospitalUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsScholarshipMedicineMedical educationEngineering ethicsWork (physics)SociologyPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

Understanding the definition of medical education scholarship and its underlying theoretical constructs is essential to constructing medical education and supporting learning across the continuum. There continues to exist uncertainty surrounding defining and enacting medical education scholarship. This uncertainty results in an inability for educators to conduct medical education and scholarship and may result in missed opportunities for educators and learners across the continuum. For this paper, the authors define medical education scholarship as endeavours that are purposefully undertaken and may surpass the borders of those traditionally defined as research or innovation. Medical education may take place in areas of discovery, integration, application, teaching, and engagement. This definition is based on works by Boyer, Glassick and Shulman. This paper describes the contribution that each of these seminal works has made to advance the definition of medical education scholarship. The differences between medical education scholarship and creative professional activities as well as daily work are explored throughout the paper. The paper concludes with a call to develop, demonstrate, promote and support medical education scholarship and the faculty who are focused on undertaking related activities.

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)0.002
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.793
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.071
GPT teacher head0.467
Teacher spread0.395 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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