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Record W4205954863 · doi:10.1097/acm.0000000000004588

Discomfort, Doubt, and the Edge of Learning

2022· article· en· W4205954863 on OpenAlexaff

Bibliographic record

VenueAcademic Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThe Wilson CentreWomen's College Hospital
Fundersnot available
KeywordsDialogical selfMentorshipFraming (construction)HumanismReflection (computer programming)Philosophy of educationHigher education

Abstract

fetched live from OpenAlex

Discomfort is a constant presence in the practice of medicine and an oft-ignored feature of medical education. Nonetheless, if approached with thoughtfulness, patience, and understanding, discomfort may play a critical role in the education of physicians who practice with excellence, compassion, and justice. Taking Plato's notion of aporia-a moment of discomfort, perplexity, or impasse-as a starting point, the author follows the meandering path of aporia through Western philosophy and educational theory to argue for the importance of discomfort in opening up and orienting perspectives toward just and humanistic practice. Practical applications of this approach include problem-posing questions (from the work of Brazilian education theorist Paulo Freire), exercises to "make strange" beliefs and assumptions that are taken for granted, and the use of stories-especially stories without endings-all of which may prompt reflection and dialogical exchange. Framing this type of teaching and learning in Russian psychologist L.S. Vygotsky's theories of development, the author proposes that mentorship and dialogical interactions may help learners to navigate through moments of discomfort and uncertainty and extend the edge of learning. This approach may give birth to a zone of proximal development that is enriched with explorations of self, others, and the world.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.344
Teacher spread0.323 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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