A Structural Equation Model on Mental Health of Korean Immigrants in Canada
Bibliographic record
Abstract
PURPOSE: The aim of this study was to construct a structural equation model that would further explain the mental health status of Korean immigrants living in Canada. METHODS: Survey using a structured questionnaire was conducted with 386 people in Canada (Vancouver and Toronto). Six instruments were used in this model. The analysis of data was done with both SPSS 14.0 for descriptive statistics and AMOS 5.0 for covariance structure analysis. RESULTS: Based on the constructed model, physical health status, immigrant life stress, self esteem, and quality of life were found to have significant direct effect on mental health. In addition, factors such as physical health status, immigrant life stress, quality of life, English proficiency, family cohesion and social support were found to indirectly affect mental health. The final modified model yielded Chi-square=34.79 (p<.001), df=13, X(2)/df=2.68, GFI=0.98, AGFI=0.94, NFI=0.95, PNFI=0.44, PGFI=0.35, RMSE=0.07 and exhibited good fit indices. CONCLUSION: This structural equation model is a comprehensive theoretical model that explains the related factors and their relationship with mental health in Korean immigrants. Findings of this study can contribute to the designing of an appropriate prevention strategy to further improve the mental health of immigrants in Canada.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".