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Record W2283447352 · doi:10.1136/medhum-2015-010731

I and Thou: learning the ‘human’ side of medicine

2016· article· en· W2283447352 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Humanities · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThouPerspective (graphical)HumanismNarrativeEpistemologySociologyMedical humanitiesNarrative medicinePsychologyPhilosophyMedicineLinguisticsMedical educationComputer science

Abstract

fetched live from OpenAlex

This essay is a reflection on the doctor-patient relationship from the perspective of two medical students, which draws on the ideas of 20th-century philosopher Martin Buber. Although Buber never wrote about medicine directly, his 'philosophy of dialogue' raises fundamental questions about how human beings relate to one another, and can thus offer valuable insights into the nature of the clinical encounter. We argue that Buber's basic word pairs, 'I-You' and 'I-It', provide a useful heuristic for understanding different modes of caring for patients, which we illustrate using examples of illness narratives from two literary works: Tolstoy's Ivan Ilych and Margaret Edson's Wit Our essay demonstrates how the humanities in general and philosophy in particular can inform a more humanistic practice for healthcare trainees and practicing clinicians alike.

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.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.323
Teacher spread0.280 · 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