The Hispanic Paradox and Older Adults’ Disabilities: Is There a Healthy Migrant Effect?
Bibliographic record
Abstract
The "Hispanic Paradox" suggests that despite rates of poverty similar to African Americans, Hispanics have far better health and mortality outcomes, more comparable to non-Hispanic White Americans. Three prominent possible explanations for the Hispanic Paradox have emerged. The "Healthy Migrant Effect" suggests a health selection effect due to the demands of migration. The Hispanic lifestyle hypothesis focuses on Hispanics' strong social ties and better health behaviors. The reverse migration argument suggests that the morbidity profile in the USA is affected when many Hispanic immigrants return to their native countries after developing a serious illness. We analyzed data from respondents aged 55 and over from the nationally representative 2006 American Community Survey including Mexican Americans (13,167 U.S. born; 11,378 immigrants), Cuban Americans (314 U.S. born; 3,730 immigrants), and non-Hispanic White Americans (629,341 U.S. born; 31,164 immigrants). The healthy migrant effect was supported with SES-adjusted disability comparable between Mexican, Cuban and non-Hispanic Whites born in the USA and all immigrants having lower adjusted odds of functional limitations than U.S. born non-Hispanic Whites. The reverse migration hypothesis was partially supported, with citizenship and longer duration in the USA associated with higher rates of SES-adjusted disability for Mexican Americans. The Hispanic healthy life-style explanation had little support in this study. Our findings underline the importance of considering nativity when planning for health interventions to address the needs of the growing Hispanic American older adult population.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".