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Record W7083307495 · doi:10.22329/uwdj.v2i1.9001

Integrating a Narrative Approach to Medicine

2024· article· en· W7083307495 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueUWill Discover Journal · 2024
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNarrativeNarrative medicineWitnessHealth careGlobeWork (physics)Experiential learningFutures contract

Abstract

fetched live from OpenAlex

This podcast was prepared for the UWill Discover Sustainable Futures Project at the University of Windsor by Outstanding Scholar student Krishali Kumar. Narrative medicine is a technique in which a physician will apply "narrative competence-that is, [the capacity to receive, interpret, co-construct, and bear witness to the stories I...] patients bring" (Peterkin, 2012, paras. 1). This podcast will explore the roots of narrative medicine, its benefits considering the current state of Canadian healthcare, and disadvantages, drawing from experiential applications in healthcare institutions across the globe and locally, including at the University of Toronto's Narrative Medicine Lab. Additionally, this podcast will suggest strategies in integrating narrative medicine in care for future healthcare professionals. The potential impact of this work is to instill in future medical providers the importance of the medical humanities as to promote the well-being of their future patients.

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)0.000
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: none
Teacher disagreement score0.963
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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