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Record W3182780947 · doi:10.1097/acm.0000000000004225

What Role Should Resistance Play in Training Health Professionals?

2021· article· en· W3182780947 on OpenAlexaff
Rachel Ellaway, Tasha R. Wyatt

Bibliographic record

VenueAcademic Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSouth Health Campus
Fundersnot available
KeywordsResistance (ecology)Agency (philosophy)Medical educationProfessional developmentPublic relationsPsychologySociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

The role that resistance plays in medicine and medical education is ill-defined. Although physicians and students have been involved in protests related to the COVID-19 pandemic, structural racism, police brutality, and gender inequity, resistance has not been prominent in medical education's discourses, and medical education has not supported students' role and responsibility in developing professional approaches to resistance. While learners should not pick and choose what aspects of medical education they engage with, neither should their moral agency and integrity be compromised. To that end, the authors argue for professional resistance to become a part of medical education. This article sets out a rationale for a more explicit and critical recognition of the role of resistance in medical education by exploring its conceptual basis, its place both in training and practice, and the ways in which medical education might more actively embrace and situate resistance as a core aspect of professional practice. The authors suggest different strategies that medical educators can employ to embrace resistance in medical education and propose a set of principles for resistance in medicine and medical education. Embracing resistance as part of medical education requires a shift in attention away from training physicians solely to replicate and sustain existing systems and practices and toward developing their ability and responsibility to resist situations, structures, and acts that are oppressive, harmful, or unjust.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.455
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.097
GPT teacher head0.457
Teacher spread0.360 · 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 designNot applicable
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

Citations59
Published2021
Admission routes1
Has abstractyes

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