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Understanding the Debate on Medical Education Research: A Sociological Perspective

2004· article· en· W2048839178 on OpenAlexaff
Mathieu Albert

Bibliographic record

VenueAcademic Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoThe Wilson Centre
Fundersnot available
KeywordsSociologyParallelsPerspective (graphical)Field (mathematics)Opposition (politics)EpistemologyEducational researchPower (physics)Engineering ethicsSocial sciencePolitical sciencePoliticsLawComputer science

Abstract

fetched live from OpenAlex

Since the mid-1990s, a debate has taken place among medical education scholars regarding the forms that research should take and the roles it should play. Editors of major journals in medical education and prominent researchers in the domain have repeatedly addressed the issue and have attempted to define what medical education research should be. The goal of this article is to look at the debate from a sociological perspective and to outline the social factors shaping it. An analysis of the texts published since 1990 addressing the issue shows that the debates can be deconstructed in four topics: epistemology, methodology, the primary purpose of medical education research, and the "quality" of the projects carried out in the domain. However, the debates can also be amalgamated and synthesized using the concept of "field" as developed by sociologist Pierre Bourdieu. A "field" refers to the configuration of power relations among individuals, social groups, or institutions within a domain of activities. Scientific fields are typically structured around a "bipolar" opposition pattern. At one pole stand those individuals who promote greater collaboration with nonscientists as well as research aimed at responding to practical needs. At the opposite pole stand those individuals who aspire to achieve independence of the field from such external constraints. The use of the concept of "field" allows us to understand the debate from a larger perspective and to establish parallels with similar debates in other scientific fields. In doing so, we will have the opportunity to learn from the experience of these other fields and be more reflective about the debate in which we engage.

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)0.006
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.363
GPT teacher head0.519
Teacher spread0.156 · 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.

Study designTheoretical or conceptual
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

Citations63
Published2004
Admission routes1
Has abstractyes

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