Factors Associated With Depressive Symptoms Among U.S. Older Chinese Immigrants
Bibliographic record
Abstract
Abstract Purpose: Chinese Americans represent the largest Asian ethnic subgroup in the United States. Depression is the most common mental health problem among older adults. However, we have a limited understanding of depressive symptoms among older Chinese immigrants. The study aimed to examine the potential factors associated with depressive symptoms among older Chinese immigrants in U.S. Methods: We recruited participants from psychiatric clinics who sought professional help in New York City. Inclusion criteria were Chinese immigrants from Asian countries 50 years or older; able to speak and understand either Mandarin or Cantonese; and had a diagnosis with major depressive disorder. Depressive symptoms were measured with Quick Inventory of Depressive Symptomatology; cognitive function was measured with Montreal cognitive assessment; sleep quality was measured with Pittsburgh Sleep Quality Index, and physical activity was measured with International Physical Activity Questionnaire. Descriptive statistics and multiple regression were performed. Results: Participants were ninety-nine Chinese older immigrants (mean age: 60.69 ± 7.62 years). Participants who had more children (p < .05), poor health status (p < .01), poor quality of life (p < .01), less social support (p < .01), and need help with activities of daily living (p < .05) had more depressive symptoms. Cognitive function, sleep quality, and physical activity were significantly associated with depressive symptoms. Conclusions & Implications: Poor cognitive function, poor sleep quality, and less physical activity were associated with depressive symptoms. Our results provide knowledge for developing culturally tailored self-management interventions for older Chinese immigrants with depressive disorder in managed care settings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".