The Social Determinants of Depression in Elderly Korean Immigrants in Canada: Does Acculturation Matter?
Bibliographic record
Abstract
Depression in old age significantly decreases the quality of life and may lead to serious consequences, such as suicide. Existing literature indicates that elderly Korean immigrants may experience higher levels of depression than other racial ethnic group elders. The purpose of this exploratory study was to investigate factors that influence depression among older Korean immigrants in Toronto. A total of 148 participants, ages 60 years or older (mean age = 74.01, SD = 8.24), completed face-to-face interviews in Korean language. Hierarchical regression analyses were conducted by adding variables in three steps: (1) demographic variables; (2) acculturation variables (years of immigration and English proficiency); and (3) social determinants (social integration variables, physical health, and financial satisfaction). Results showed that acculturation factors were not associated with depression. Instead, social determinants variables, including lower physical health status and lower financial status, living alone, and lower level of social activity, predicted higher level of depressive symptoms, along with lower education. The final regression model explained about 37% of variance of depression in the sample. These results suggest that social determinants, not acculturation, are important factors explaining the levels of depression in Korean immigrant elders living in a metropolitan city in Canada. Implications for practice are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".