Bibliographic record
Abstract
The purpose of this descriptive study was to explore the factors influencing quality of life among Korean immigrants in Canada. Survey using a structured questionnaire was conducted with 386 people in Canada (Vancouver and Toronto). The data collected was SPSS 17.0 t-test, ANOVA, Pearsons correlation and multiple regression was analyzed. Quality of life showed a significantly positive correlation with the following variables: self-esteem (r=?.43, p＜.001), social support (r=?.38, p＜.001), and family cohesion (r=?.20, p＜.001). And significantly negative correlation with the following variables: depression (r=.44, p＜.001). The result of the multiple regression analysis to identify the predicting variables to quality of life showed that among the independent variables, self-esteem (β=?.19, p=.001), depression (β=.32, p＜.001), and social support (β=?.22, p＜.001) influence on to quality of life of Korean immigrants in Canada. Quality of life was accounted for 29.0% of variance by these factors. This result that psychological factors such as self-esteem, depression, and social support. This study also investigated that the following socio-demographic factors significantly affect quality of life: marital state, immigration life satisfaction and physical health status. Thus, the most effective promotion of quality of life must focus on improving immigration life satisfaction, physical health status, self-esteem, social support, and prevention of depression. By addressing the factors related to quality of life, and comparing each influence, this study can contribute to designing an appropriate prevention strategy in further improve immigrants stress. (Korean J Str Res 2010;18:363∼370)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".