U.S.-Born Older Asians’ Diminishing Health Advantage Relative to Other Racial Groups, 2005–2022
Bibliographic record
Abstract
OBJECTIVES: Previous studies have shown that Asian Americans have lower disability and mortality rates than other racial/ethnic groups, indicating a more favorable health profile. This phenomenon is often attributed to the large proportion of Asians being foreign-born and positively selected. However, the health status of U.S.-born older Asians and its trend over time remain unclear. METHODS: We used data from the American Community Survey to describe changes in age-adjusted disability prevalence among native-born older Asians relative to other racial/ethnic groups since 2005. RESULTS: Although U.S.-born Asians aged 50 and older had lower disability prevalence than other racial/ethnic groups in 2005-09, their prevalence stagnated over time, while other groups experienced reductions. Consequently, the health advantage of U.S.-born older Asians diminished between 2005 and 2022. A key explanation for this phenomenon is a relative decline in socioeconomic status (SES) among older Asians compared to Whites over time. Asians experienced stagnation in high school attainment and a clear decline in the proportion of the population above the bottom income quintile, while Whites (and most others) experienced improvement in both SES measures. Furthermore, U.S.-born older Asians with low SES experienced an increase in disability, a trend not observed in any other racial or nativity group. We found suggestive evidence that declining community and family support among native-born older Asians may have also eroded their health advantage. DISCUSSION: The "model minority" stereotype increasingly misrepresents the well-being of U.S.-born older Asians, a population that requires further research attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".