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Record W2095257761 · doi:10.2105/ajph.2005.070169

IMMIGRANT GENERATIONAL STATUS AND ETHNIC DIFFERENCES IN HEALTH

2005· letter· en· W2095257761 on OpenAlexaboutno aff
William J. McCarthy, Yvonne N. Flóres, Hong Zheng, Thomas Hanson

Bibliographic record

VenueAmerican Journal of Public Health · 2005
Typeletter
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersTobacco-Related Disease Research Program
KeywordsImmigrationAcculturationEthnic groupCitizenshipPopulationRefugeeLatin AmericansCountry of originMedicineDemographyGerontologyPolitical scienceGeographySociologyPolitics

Abstract

fetched live from OpenAlex

We read with interest 2 recent reports using population-level epidemiological data to make inferences about ethnic differences in illicit drug use status1 or health status.2 Both reports were remiss in not attributing more importance to the influence of immigration history and immigrant generational status. Particularly for countries of origin distant from the United States and Canada and from which most immigrants came voluntarily (as opposed to immigrating as refugees), immigrants tend to have better health status and better health practices than is the norm either in their country of origin or among second-generation or later-generation persons sharing their national heritage.3 This phenomenon is known as the “healthy immigrant” effect.4,5 We applaud Delva and associates’ examination of the effects of acculturation, in which the authors used a question about first language spoken as a child (English or Spanish) as a proxy for acculturation status. First-generation immigrants are more likely to use a language other than English in the home than second or later generations.6 However, we take issue with the authors’ inclusion of respondents of Cuban and Puerto Rican origin in this examination. Most Cuban immigrants have come to the United States as refugees, and Puerto Rican immigrants have had many rights of US citizenship and much exposure to US culture before immigrating to the continental United States. Not surprisingly, Delva and associates found that only for Mexican Americans and “other Latin American” students was the language question a significant predictor of marijuana use or heavy drinking. Consistent with the healthy immigrant effect, use of Spanish in these populations was associated with a reduced likelihood of reporting marijuana use or heavy alcohol drinking. To their credit, Wu and Schimmele did incorporate a dummy variable called “immigrant,” reflecting whether a respondent had been born in Canada or not.7 Again, consistent with the healthy immigrant effect, they found functional health to be greater in first-generation Canadians than in Canadians who were born in Canada. This effect has elsewhere been observed to be particularly pronounced in Blacks, resulting in 7.8 to 9.4 years of extra longevity for first-generation immigrant Blacks compared with native-born Blacks.7 Wu and Schimmele should be more cautious in their speculative explanation for the superior functional health status of Canadian Blacks compared with the health of US Blacks. It would be more parsimonious to attribute the observed national differences in Black mortality rates to national differences in immigration history than to national differences in access to health care. Researchers examining racial/ethnic health disparities in countries characterized by substantial recent immigration should consider and report both immigration history and immigrant generational status because of these factors’ high potential for confounding with ethnicity as contributors to observed disparities in health status.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.085
GPT teacher head0.379
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2005
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Public HealthSame topicMigration, Health and TraumaFrench-language works237,207