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Comparison of the anesthesia resident training systems between China and North America

2010· article· en· W3032713907 on OpenAlexaboutno aff
Hui Ju, Yi Feng

Bibliographic record

VenueChinese Journal of Medical Education · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)ChinaTraining systemPresentation (obstetrics)Medical educationResidency trainingProcess (computing)MedicinePsychologyAnesthesiaPolitical scienceContinuing educationSurgeryComputer scienceGeography

Abstract

fetched live from OpenAlex

A structured anesthesia resident training system is a crucial part in the anesthetist training process. In China, the Standard Resident Training System has just developed in recent years, but without uniform standard in country wide. Compared with the longstanding and more structured resident training system of North America, we still have a lot to learn in order to improve the comprehensive ability of Chinese anesthesia residents and make them more competitive globally. In this article, the anesthesia resident training systems of China and North America were compared in the following aspects: the goal of resident training; training process and testing methods; allocation after trained; and training details regarding teaching skill, presentation and leadership. In the last part of this article, the author concluded some experience in anesthesia fellowship training in Canada of 7 Chinese anesthetists. Key words: Anesthesia; Resident training; China; North America; Comparison

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.353
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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