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Critical thinking in clinical medicine: what is it?

2012· article· en· W1502010468 on OpenAlexaff
Mona Gupta, Ross Upshur

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2012
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoUniversité de Montréal
Fundersnot available
KeywordsCritical thinkingHonestyContext (archaeology)PersuasionIntimidationQuality (philosophy)EpistemologyPsychologyEngineering ethicsSociologySocial psychology

Abstract

fetched live from OpenAlex

In this paper, we explore the recent emphasis, in various medical contexts, of the term 'critical' or the notion of 'being critical'. We identify various definitions of being critical and note that they differ strikingly. What are these different uses of the term trying to capture that is important in clinical medicine and medical education? We have analysed these qualities as responsibilist, epistemic virtues. We believe that a virtues approach is best able to make sense of the non-cognitive elements of 'being critical', such as the honesty and courage to question claims in the face of persuasion, authority or social pressure. Medical educators and professional bodies seem to agree that being critical is important and desirable. Yet, it is unclear how this quality can be optimally fostered and balanced with the constraints that act upon individual practitioners in the context of institutional medicine including professional standards and the demands of the doctor-patient relationship. Other constraints such as authoritarianism, intimidation and financial pressures may act against the expression of being critical or even the cultivation of critical thinking. The issue of the constraints on critical thinking and the potential hazards it entails will require further consideration by those who encourage being critical in medicine.

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)0.133
metaresearch head score (Gemma)0.503
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.496
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1330.503
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.276
GPT teacher head0.645
Teacher spread0.369 · 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; both teacher heads agree on what is shown here.

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

Citations35
Published2012
Admission routes1
Has abstractyes

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