Comparison of Disability Rates Among Older Adults in Aggregated and Separate Asian American/Pacific Islander Subpopulations
Bibliographic record
Abstract
OBJECTIVES: We assessed the prevalence and adjusted odds of 4 types of disability among 7 groups of older Asian American/Pacific Islander (AAPI) subpopulations, both separately and aggregated, compared with non-Hispanic Whites. METHODS: Data were from the nationally representative 2006 American Community Survey, which included institutionalized and community-dwelling Hawaiian/Pacific Islander (n = 524), Vietnamese (n = 2357), Korean (n = 2082), Japanese (n = 3230), Filipino (n = 5109), Asian Indian (n = 2942), Chinese (n = 6034), and non-Hispanic White (n = 641 177) individuals aged 55 years and older. The weighted prevalence, population estimates, and odds ratios of 4 types of disability (functional limitations, limitations in activities of daily living, cognitive problems, and blindness or deafness) were reported for each group. RESULTS: Disability rates in older adults varied more among AAPI subpopulations than between non-Hispanic Whites and the aggregated Asian group. Asian older adults had, on average, better disability outcomes than did non-Hispanic Whites. CONCLUSIONS: This study provides the strongest evidence to date that exclusion of institutionalized older adults minimizes disparities in disabilities between Asians and Whites. The aggregation of Asians into one group obscures substantial subgroup variability and fails to identify the most vulnerable groups (e.g., Hawaiian/Pacific Islanders and Vietnamese).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".