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Record W1556788499

Undergraduate surgical training: variations in program objectives and curriculum implementation across Canada.

2006· article· en· W1556788499 on OpenAlexaffabout
Shawn Forbes, Peter L. Fitzgerald, Daniel W. Birch

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSpecialtyCurriculumHumanitiesMedical educationOccupational trainingLibrary sciencePedagogyFamily medicinePsychology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Although nationally recognized learning objectives for undergraduate surgical education exist, the extent to which Canadian medical schools follow these guidelines has never been established. METHODS: We distributed a survey to all program directors and clinical-teaching-unit coordinators for undergraduate surgery at Canada's 16 medical schools, and subsequently assessed the perceived emphasis placed on learning objectives and student performance, and the impact of instructional tools and teaching locations. RESULTS: Program directors in 15 medical schools responded to the survey. We identified a wide variation in the emphasis placed on basic learning objectives as well as specialty specific learning objectives. The length of rotations, methods of instruction and tools used to grade student performance also varied widely. CONCLUSIONS: Our findings suggest significant variation in the design and implementation of undergraduate surgical education in Canada. This study may serve as a basis for reassessing learning objectives in Canadian undergraduate surgical education.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.016
GPT teacher head0.331
Teacher spread0.315 · 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.

Study designObservational
DomainMethods
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

Citations26
Published2006
Admission routes2
Has abstractyes

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