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Record W3038077850 · doi:10.1007/s10459-020-09977-8

Interdisciplinarity in medical education research: myth and reality

2020· article· en· W3038077850 on OpenAlexafffund
Mathieu Albert, Paula Rowland, Farah Friesen, Suzanne Laberge

Bibliographic record

VenueAdvances in Health Sciences Education · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversité de MontréalSt. Michael's HospitalThe Wilson CentreUniversity of TorontoUniversity Health Network
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBibliometricsTypologySociologyDisciplineEducational researchScholarshipNarrativeNarrative inquiryLibrary scienceSocial scienceAnthropologyPolitical science

Abstract

fetched live from OpenAlex

The medical education (Med Ed) research community characterises itself as drawing on the insights, methods, and knowledge from multiple disciplines and research domains (e.g. Sociology, Anthropology, Education, Humanities, Psychology). This common view of Med Ed research is echoed and reinforced by the narrative used by leading Med Ed departments and research centres to describe their activities as "interdisciplinary." Bibliometrics offers an effective method of investigating scholarly communication to determine what knowledge is valued, recognized, and utilized. By empirically examining whether knowledge production in Med Ed research draws from multiple disciplines and research areas, or whether it primarily draws on the knowledge generated internally within the field of Med Ed, this article explores whether the characterisation of Med Ed research as interdisciplinary is substantiated. A citation analysis of 1412 references from research articles published in 2017 in the top five Med Ed journals was undertaken. A typology of six knowledge clusters was inductively developed. Findings show that the field of Med Ed research draws predominantly from two knowledge clusters: the Applied Health Research cluster (made of clinical and health services research), which represents 41% of the references, and the Med Ed research cluster, which represents 40% of the references. These two clusters cover 81% of all references in our sample, leaving 19% distributed among the other knowledge clusters (i.e., Education, disciplinary, interdisciplinary and topic centered research). The quasi-hegemonic position held by the Applied Health and Med Ed research clusters confines the other sources of knowledge to a peripheral role within the Med Ed research field. Our findings suggest that the assumption that Med Ed research is an interdisciplinary field is not convincingly supported by empirical data and that the knowledge entering Med Ed comes mostly from the health research domain.

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)0.113
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.598

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1130.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.011
Science and technology studies0.0190.184
Scholarly communication0.0450.046
Open science0.0030.021
Research integrity0.0080.013
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 head0.099
GPT teacher head0.558
Teacher spread0.459 · 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 designTheoretical or conceptual
DomainMethods
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

Citations42
Published2020
Admission routes2
Has abstractyes

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