Stigma Beliefs of Asian Americans with Depression in an Internet Sample
Bibliographic record
Abstract
OBJECTIVE: To study the beliefs of Asian Americans with depression about stigma associated with depression treatment among friends, employers, and family. METHOD: Participants completed the Center for Epidemiologic Studies-Depression Scale (CES-D) anonymously on the Internet. In this cross-sectional design, those who screened positive for depression were asked questions regarding stigma (n = 68 656). We used analysis of variance (ANOVA) and analysis of covariance (ANCOVA) to compare Asian Americans with whites and also to make comparisons by age and sex. Further, we stratified for Asian Americans and used ANOVA and ANCOVA to compare age and sex. We used linear regression to assess how stigma beliefs were associated with self-reported need for depression treatment. RESULTS: Asian Americans overall had greater stigma beliefs than did whites for all 3 stigma outcomes (P < 0.001), especially those related to family. Although this same pattern existed for subjects aged between 16 and 29 years and between 30 and 45 years (P < 0.001), among those aged under 16 years, this existed for family stigma (P < 0.001) but not for friends or employer stigma. In our stratified analyses among Asian Americans, male participants had greater stigma beliefs than did female participants for friends (P < 0.001) and employer (P < 0.05) but not for family. CONCLUSIONS: The pattern of Asian Americans having greater stigma levels than whites may be changing among younger Asian Americans because of acculturation. Also, among Asian Americans, unlike previous research showing no sex differences for stigma, we show that male participants had greater stigma levels than did female participants. Future directions should include measuring stigma after culture-specific interventions.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".