IMMIGRANT GENERATIONAL STATUS AND ETHNIC DIFFERENCES IN HEALTH
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».