Adverse Childhood Experiences and Ethnic Identity in Asian Americans: Associations with Symptoms of Posttraumatic Stress, Depression, Anxiety, and Binge Drinking
Bibliographic record
Abstract
Asian Americans experience high rates of adverse childhood experiences (ACEs) but are significantly underrepresented in ACEs research. Despite evidence indicating that ACEs contribute to increased psychological distress and substance use among minoritized emerging adults and that a strong sense of ethnic identity can mitigate these impacts, no study has exclusively examined these relationships among Asian Americans. This study investigated (1) how ACEs relate to symptoms of posttraumatic stress, depression, anxiety, and binge drinking; and (2) the strength of ethnic identity as a moderator in this sample. Second-generation and one-and-a-half generation Asian Americans (N = 199, aged 18–29, 53% East Asian, 30% South Asian, 17% Southeast Asian) were recruited from Amazon Mechanical Turk and a northeastern university in the U.S. to complete an online survey. Multivariate linear and binary logistic regressions revealed that ACEs significantly predicted higher symptoms of posttraumatic stress (B = 3.00, p < .001), depression (B = 2.36, p < .001), and anxiety (B = 1.33, p = .002), and an increased odds of binge drinking (OR = 1.30, 95% CI [1.07, 1.58]). The strength of ethnic identity did not significantly moderate outcomes; however, stronger ethnic identity was independently significantly associated with lower anxiety symptoms (B = −2.89, p = .01). Among Asian American emerging adults, ACEs are associated with psychological distress and binge drinking. However, unlike in other minoritized groups, ethnic identity did not protect against these outcomes, suggesting the need to identify alternative culturally-relevant protective factors in Asian Americans.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".