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Record W4415245887 · doi:10.1080/1369183x.2025.2560577

Does a healthy immigrant effect exist for internal migrants? Findings from a representative sample of 5.4 million older Americans

2025· article· en· W4415245887 on OpenAlexaff
Katherine Marie Ahlin, Alyssa McAlpine, Esme Fuller‐Thomson

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImmigrationSample (material)Internal migrationEthnic group

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.446
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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