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Record W2900675991 · doi:10.17496/kmer.2017.19.2.63

Concept and Development of Resident Training Program for General Competencies

2017· article· en· W2900675991 on OpenAlexaboutno aff
Sun Woo Lee

Bibliographic record

VenueKorean Medical Education Review · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Training systemMedical educationPsychologyMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

Resident training programs in South Korea lag far behind that of advanced countries. Given the problems the current system in South Korea has, it is time to consider a new resident training system, resident training for general competencies. Training for the general competencies was practiced in medical fields in advanced countries such as the USA, Canada, and the UK as early as 20 years ago. This system has rendered itself a key component of resident training. Although a few theoretical procedures on general competencies have been practiced in South Korea, the awareness of this concept is still very weak, and the application of the theory to actual training is a long way off from becoming effective. It is urgent for South Korea to adopt competency- and outcome-based training for general competencies. To this end, the knowledge of the concept of this type of training should be improved. Also, the system should be carefully designed to cover a doctor’s whole career, and be applied immediately. The competency- and outcome-based training for general competencies is a system that assures high level qualifications. It reflects the needs of our society under the recognition that a professional organization should be committed and accountable in order to respond to social demands. As the benefits of the new training system reach the public and medical care consumers, training-related expenses should be borne by social costs.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.438
Teacher spread0.369 · 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
GenreMethods

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

Citations5
Published2017
Admission routes1
Has abstractyes

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