Depression Disparities in Older Korean and Chinese Immigrants in the United States
Bibliographic record
Abstract
Abstract This meta-analysis compares depression levels among Chinese and Korean immigrants aged 60 and older in the United States. Studies published between 2010 and 2024 were synthesized using standardized mean differences (SMDs) from a random-effects model. Korean immigrants reported higher depressive symptoms than Chinese immigrants when assessed with the Patient Health Questionnaire (PHQ) (SMD = 0.0778, SE = 0.0193, p < .0001, 95% CI [0.0409, 0.1147]), but no significant difference emerged with the Geriatric Depression Scale (GDS) (SMD = -0.0060, SE = 0.0625, p = 0.9233, 95% CI [-0.1284, 0.1164]). Within-group analyses indicated that GDS produced higher depression scores than PHQ for both groups. Among Chinese immigrants, PHQ scores were significantly lower than GDS scores (SMD = -0.2430, SE = 0.0411, p < .0001, 95% CI [-0.3235, -0.1625]), suggesting PHQ may underestimate depression. Likewise, Korean immigrants recorded lower PHQ scores than GDS scores (SMD = -0.1601, SE = 0.0569, p = 0.0049, 95% CI [-0.2715, -0.0486]). Korean immigrants’ CES-D scores were also lower than GDS scores (SMD = -0.0998, SE = 0.0417, p = 0.0167, 95% CI [-0.1816, -0.0181]). Potential contributors included acculturation, social support, socioeconomic status, language barriers, healthcare availability, and intergenerational dynamics. These findings underscore the importance of culturally tailored screening and interventions. Research should address measurement disparities, investigate longitudinal patterns, and develop targeted strategies to reduce depression among older Asian immigrants. This could inform policies and optimize mental health outcomes for this vulnerable population. Clinicians and policymakers should prioritize culturally responsive mental health screening, interventions, and policy reforms.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".