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Record W4361765491 · doi:10.17496/kmer.2018.20.3.128

Overseas Residency Training Systems and Implications for Korea

2018· article· en· W4361765491 on OpenAlexaboutno aff
Sun Woo lee

Bibliographic record

VenueKorean Medical Education Review · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMilestoneMedical educationCurriculumTraining (meteorology)Core competencyResidency trainingMedicineTraining systemFamily medicinePsychologyPolitical scienceContinuing educationBusinessPedagogy

Abstract

fetched live from OpenAlex

Medical education, competency, and outcome-based medical education started as part of the basic medical education curriculum in advanced countries 20 years ago, and such an approach was adopted in residency training. General competency training is at the core of residency training in advanced countries, and it goes beyond competency and outcome-based training to the extent that in a milestone training system, competency development is expected and measured with set competency achievements at each level. Recently, for the purpose of ensuring that doctors uphold patient safety and fulfill their obligations, entrustable professional activities (EPA) were applied at the beginning of residency when doctors move away from clinical trials and start actual care. The adoption of EPA in all residency training curriculum has spread very rapidly in the United States, United Kingdom, and Canada. Presently, Korea lags behind other countries significantly as the adoption of competency and outcome-based medical education in residency training has just begun. It is time to identify the current state of the Korean residency training system, and then design and practice a well-established system with a long-term view based on cooperation across the whole medical industry.

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 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)0.002
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.528
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.057
GPT teacher head0.424
Teacher spread0.367 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

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