Does a healthy immigrant effect exist for internal migrants? Findings from a representative sample of 5.4 million older Americans
Bibliographic record
Abstract
Immigrants in North America and Europe typically have better health outcomes than the native-born population in the host country. Less is known about whether this occurs among internal migrants. This study aimed to: (1) Determine if older Americans who live in their natal state have a higher prevalence and odds of disabilities (memory problems, hearing problems, vision problems, limitations in activities of daily living, functional limitations) compared to internal migrants; (2) Identify if older American immigrants have a higher prevalence and odds of disabilities compared to internal migrants. Chi-square and logistic regression analyses were conducted using 10 years of nationally representative data from the American Community Survey with 5.4 million older adults. Compared to internal migrants, older adults living in their natal state had significantly higher odds of all disabilities after adjusting for age, sex, and race. Controlling for education partially attenuated these associations for four of the disability outcomes. After adjustment for education, age, sex and race, the odds of four types of disabilities were significantly lower for international immigrants compared to internal migrants. Our findings add to the growing body of research exploring elements of the healthy migrant effect for internal migrants as well as international migrants.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".