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Development of a Medical Humanities Program at Dalhousie University Faculty of Medicine, Nova Scotia, Canada, 1992-2003

2003· article· en· W2030183026 on OpenAlexaffabout
Jock Murray

Bibliographic record

VenueAcademic Medicine · 2003
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNova scotiaMedical humanitiesCurriculumMedical educationHumanitiesNova (rocket)Medical schoolMedical ethicsMulticulturalismSociologyMedicinePedagogyPolitical scienceArtLawEngineering

Abstract

fetched live from OpenAlex

The Medical Humanities Program at Dalhousie University Faculty of Medicine in Nova Scotia, Canada, was initiated in 1992 to incorporate the medical humanities into the learning and experiences of medical students. The goal of the program was to gain acceptance as an integral part of the medical school. The program assumed a broad concept of the medical humanities that includes medical history, literature, music, art, multiculturalism, philosophy, epistemology, theology, anthropology, professionalism, history of alternative therapies, writing, storytelling, health law, international medicine, and ethics. Phase I of the program has provided the same elective and research opportunities in the medical humanities that are available to the students in clinical and basic sciences, and has encouraged and legitimized the involvement of the humanities in the life and learning of the medical student through a wide array of programs and activities. Phase II will focus on further incorporation of the humanities into the curriculum. Phase III will be the development of a graduate program in medical humanities to train more faculty who will incorporate the humanities into their teaching and into the development of education programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.003

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 head0.064
GPT teacher head0.335
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2003
Admission routes2
Has abstractyes

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