Characterizing Mental Health Status and Service Utilization in Chinese Americans With Type 2 Diabetes in New York City: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Emerging evidence indicates that individuals with type 2 diabetes (T2D) are more prone to mental health issues than the general population; however, there is a significant lack of data concerning the mental health burden in Chinese Americans with T2D. OBJECTIVE: The aim of this study was to explore the comorbid mental health status, health-seeking behaviors, and mental service utilization among Chinese Americans with T2D. METHODS: A cross-sectional telephone survey was performed among 74 Chinese Americans with T2D in New York City. We used standardized questionnaires to assess mental health status and to gather data on mental health-seeking behaviors and service utilization. Descriptive statistics were applied for data analysis. RESULTS: A total of 74 Chinese Americans with T2D completed the survey. Most participants (mean age 56, SD 10 years) identified as female (42/74, 57%), were born outside the United States (73/74, 99%), and had limited English proficiency (71/74, 96%). Despite nearly half of the participants (34/74, 46%) reporting at least one mental health concern (elevated stress, depressive symptoms, and/or anxiety), only 3% (2/74) were currently using mental health services. Common reasons for not seeking care included no perceived need, lack of information about Chinese-speaking providers, cost, and time constraints. The cultural and language competence of the provider was ranked as the top factor related to seeking mental health care. CONCLUSIONS: Chinese Americans with T2D experience relatively high comorbid mental health concerns yet have low service utilization. Clinicians may consider team-based care to incorporate mental health screening and identify strategies to provide culturally and linguistically concordant mental health services to engage Chinese Americans with T2D.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".