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Record W2970230462 · doi:10.1111/medu.13828

Myths and social structure: The unbearable necessity of mythology in medical education

2019· article· en· W2970230462 on OpenAlexaff
Maria Athina Martimianakis, Jon C. Tilburt, Barret Michalec, Frederic W. Hafferty

Bibliographic record

VenueMedical Education · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSickKids FoundationThe Wilson CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMythologyNarrativeMeaning (existential)ScholarshipStorytellingSociologyEpistemologyLiteratureLawPhilosophyPolitical scienceArt

Abstract

fetched live from OpenAlex

CONTEXT: Myth busting engages scholars in the critical examination of commonly accepted but poorly evidenced claims with the goal of instilling quality and trust in knowledge making. The debunking of such knowledge "myths" and associated misguided practices purportedly serves to avert resources and attention from wasteful and dangerous scholarship. We address the myth that "all myths in medical education deserve to be busted". METHODS: Using a critical narrative approach, we searched the medical education literature for orientations to myths and myth busting, and reviewed this literature analytically drawing from the sociology of science and Merton's concepts of manifest and latent functions. The results of this analysis are presented in the form of a narrative that deploys the articles reviewed to explore the utility of myth busting for medical education reform and begins with a brief exploration of the etymology of "myth" and how meaning making is related to symbols, practices and storytelling. RESULTS: Our analysis revealed the important function of myths in the social practice of medical education and practice. A deconstruction of five salient examples of the contemporary myth in medical education (the myth of the "ideal candidate", the myth of "cut-throats", the myth of "cadaver stories", the myth of "learning styles", and the myth of "patient information leaflets") demonstrates that myths continue to have material effects even after they have been busted. CONCLUSIONS: Our analysis makes evident that myth busting disrupts, renegotiates and reconstitutes socio-epistemic relationships rather than simply correcting falsehoods. We also argue that myths play important and inescapable roles in the social practice of medical education and the negotiation of values, and in constructing the conditions for group change and transformation. Imperatives related to humanism, compassion and patient engagement offer a healthy humanising counter-mythologising that we suggest must survive any contemporary myth-busting endeavour aimed at improving medical education practice.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.335
Teacher spread0.330 · 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 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

Citations17
Published2019
Admission routes1
Has abstractyes

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