MétaCan
Menu
Back to cohort
Record W3025132482 · doi:10.1503/cjs.005319

Humanistic education in surgery: a “patient as teacher” program for surgical clerkship

2020· review· en· W3025132482 on OpenAlexaffvenueabout
Jory S. Simpson, Stella Ng, Emilia Kangasjarvi, Csilla Kalocsai, Aimee Hindle, Arno K. Kumagai, Tulin Cil, Darlene Fenech, Najma Ahmed, Ori D. Rotstein

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWomen's College HospitalUniversity of TorontoUniversity Health NetworkSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePsychosocialHumanismMedical educationSession (web analytics)Patient educationNursingPsychiatry

Abstract

fetched live from OpenAlex

Summary: Surgeons are frequently perceived by medical students to be uncompassionate, resolute and individualistic. Surgical education often prioritizes teaching and learning approaches that perpetuate these perceptions. In other specialties, engaging patients in education has shown promise in refocusing attention from the technical and procedural aspects of care toward the humanistic and social aspects. Despite proven favourable outcomes for both patients and students in many clinical areas, a "patient as teacher" approach to surgical education has yet to be adopted widely in Canada. A patient as teacher program was developed for surgical clerks at the University of Toronto with the goal of emphasizing the humanity of the patient, the psychosocial impact of a surgical diagnosis of breast cancer on patients and their families, and the social and humanistic roles for surgeons in providing patient-centred care. We report on the program's development process and pilot session.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.104
GPT teacher head0.407
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of SurgerySame topicInnovations in Medical EducationFrench-language works237,207