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Record W2768886842 · doi:10.17496/kmer.2017.19.3.129

Introduction of Premedical Curriculum at the College of Medicine, The Catholic University of Korea

2017· article· en· W2768886842 on OpenAlexaboutno aff
Dong-Mi Yoo, Wha Sun Kang

Bibliographic record

VenueKorean Medical Education Review · 2017
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceCurriculumLiberal arts educationCreativityMedical educationQuarter (Canadian coin)Medical schoolPsychologyMedicinePedagogyPolitical scienceHigher education

Abstract

fetched live from OpenAlex

Premed education in the college of medicine at the Catholic University of Korea aims to promote student creativity and excellence in accordance with the mission of the college: to have a sense of calling, leadership, and competency. The Catholic Medical College premed curriculum includes 75 credits which are composed of 65 credits for required courses and 10 credits for elective courses. It consists of courses in basic science, medical science, liberal arts and humanities (premedical OMNIBUS). It also involves community programs in ‘Vision and Mission,’ ‘Leadership Training,’ and ‘Academic Conference.’ In addition, students are allowed self-directed choice of their courses and learning for one quarter.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.003

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 head0.021
GPT teacher head0.357
Teacher spread0.336 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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