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Record W2963842902 · doi:10.17496/kmer.2018.20.1.51

Competency‐Based Psychiatry Residency Training Program Development in South Korea

2018· article· en· W2963842902 on OpenAlexaboutno aff
Yeong Gi Kyeon, Jong‐Woo Kim, Se-Hoon Shim, InKi Sohn, Jeongseok Seo, KangUk Lee

Bibliographic record

VenueKorean Medical Education Review · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsResidency trainingTraining (meteorology)Medical educationMedicinePsychologyFamily medicineContinuing educationGeography

Abstract

fetched live from OpenAlex

Psychiatry residency training in South Korea currently has many limits in developing proper competencies of residents. To address this problem, the Korean Neuropsychiatric Association has been developing a new competency-based training program since 2015, using the educational systems of advanced countries such as Canada, the United Kingdom, the United States, and Australia as references. It was found that within the referenced countries’ residency training systems, objectives based on competencies are stated in detail by psychiatric topics as well as various assessment methods and feedback about the resident’s competency level. In addition, we surveyed psychiatric resident training hospitals, and found that more than 80% of the respondents answered positively in reference to the new training program. This paper briefly reviews competency-based residency training systems of advanced countries and compares them to the current training program in South Korea. Many resources are needed to run a new competency-based training program, and governmental supports are essential to improve the quality of the residency training system.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.034
GPT teacher head0.391
Teacher spread0.356 · 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 designOther design
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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