Depression in Asian Americans: Does Generational Status and Acculturation Predict Severity and Type of Symptoms?
Bibliographic record
Abstract
Despite being one of the most prevalent mental disorders, accurate identification and treatment of depression may be hindered by the diversity of symptom presentation across, and even within, cultures. This study sought to identify how severity and type of depressive symptoms (psychological vs. somatic) vary by acculturation and generational status among Asian Americans. A sample of 82 U.S.-born and 81 first-generation participants completed an online anonymous survey (Patient Health Questionnaire, Somatic Symptoms Scale, Vancouver Index of Acculturation). U.S.-born participants reported higher depression symptom severity than first-generation participants (U = 2,386, p = .002, η² = .06), which aligned with the hypotheses. Inconsistent with our hypotheses, somatic symptoms of depression did not differ between U.S.-born and first-generation Asian Americans (U = 2,835, p = .11, η² = .02). Symptom severity had a weak negative correlation with acculturation to mainstream culture, r(145) = −.23, p = .006, and no correlation with heritage culture orientation, r(145) = −.16, p = .06, which also did not support our predictions. Consistent with previous research, depression severity was higher in U.S.-born Asian Americans, but this difference did not appear related to acculturation. Future research should seek to understand what aspects of generational status are tied to depression severity. Although previous theorizing suggested that Asians express depression more somatically, these findings add confidence to the small body of literature that suggests this is not the case. These findings may help improve the accuracy of diagnosis, leading to improved treatment of depression for patients of Asian heritage.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".