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Record W2565527434

한인 유학생의 적응에 영향하는 요인

2012· article· ko· W2565527434 on OpenAlexaboutno aff
윤숙희

Bibliographic record

Venuenot available
Typearticle
Languageko
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCoping (psychology)Analysis of varianceTest (biology)DemographyClinical psychologyMedicineSociology
DOInot available

Abstract

fetched live from OpenAlex

Objectives: This study was to investigate the factors influencing adaptation of Korean students studying abroad. Methods: The subjects were 135 Korean students attending in middle and high school, and college in Canada. Data was collected from December 10 2008 to January 20 2009. Data was analyzed by T-test, ANOVA, X2-test, Fisher`s exact test, Pearson coefficient of correlation and multiple regression analysis using SPSS for Windows, version 19.0. Results: Academic stress level was below the average, and examination stress was highest and relationship stress with teachers was lowest among stress subscales. Inactive coping strategy was used by 93.7% of subjects. Academic stress was different from gender, age, course of studying, period of study abroad, and english level of subjects. Academic stress was significantly related with age and period of study abroad. Age was significantly related with period of study abroad and english level. Period of study abroad was significantly related with english level and adaptation level. English level was significantly related with adaptation level. Period of study abroad and english level effect on adaptation level. Conclusions: Academic stress of Korean students studying in Canada was lower. However, stress management program would be needed for the students, who couldn`t adapt and use active coping strategy.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.054
GPT teacher head0.388
Teacher spread0.334 · 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 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
Published2012
Admission routes1
Has abstractyes

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