A Brief Review of Mental Health Issues among Asian and Pacific Islander Communities in the U.S.
Bibliographic record
Abstract
The purpose of this paper is to provide a brief summary of mental health issues among Asian and Pacific Islander (API) communities in the U.S. APIs include individuals from Far East Asia (e.g., Korea, China), Central Asia (e.g., Afghanistan, Uzbekistan), South Asia (e.g., India, Pakistan), South East Asia (e.g., Thailand, Philippines), Western Asia (e.g., Iran, Saudi Arabia), and Pacific islands (e.g., Hawaii, Samoa, Mariana island, Fiji, Palau, French Polynesia, Marshall Islands, Micronesia, New Zealand, Tokelau islands, Niue, and Cook Islands). Collectively they speak more than one hundred languages and dialects. Such a diversity across the API community presents unique challenges and opportunities for research, education, and practice. The existing body of literature on mental health issues in API communities is marred by the lack of high-quality data and insufficient degrees of disaggregation. Such a knowledge gap hindered our ability to develop culturally and linguistically tailored interventions, and in turn, API communities have experienced mental health disparities and mental health services' disparities. To move the field forward, future research effort with APIs should focus on articulating variations across different API subgroups, identifying what explains such variations, and examining the implications of such variations to research, practice, education, and policy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".