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Enregistrement W2756631898

Black Immigrant Children, Insurance Status and Neighborhood Characteristics: A Study by Length of Time in the United States Using Data from the National Survey of Children’s Health

2024· article· en· W2756631898 sur OpenAlexaboutno aff
Ndidiamaka Amutah‐Onukagha, Michelle Gardner, Laurén A. Doamekpor, Lauren Juliette Ramos

Notice bibliographique

RevueProject Muse (Johns Hopkins University) · 2024
Typearticle
Langueen
DomainePsychology
ThématiqueMigration, Health and Trauma
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationSocioeconomic statusPovertyQuarter (Canadian coin)Health careForeign bornPopulationMedicineDemographyGeographyEconomic growthEnvironmental healthSociologyEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

INTRODUCTIONThe United States is home to 20 percent of the world's foreign-born population; more than any other nation in the world, with over one million new legal immigrants arriving annually (Martin and Midgely, 2010). As the number of foreign-born in the United States steadily increases, so does the number of children of immigrant parents. Children in immigrant families, defined as those having at least one foreign-born parent, comprised 20 percent of all US children in 2000 (United States Census Bureau, 2004). In 2010, the number of immigrant children rose to nearly a quarter of all US children (Grieco et al., 2012, 6). As many immigrant families experience poor health outcomes and inequitable access to care, so do children in these families. As many as 21 percent of children in immigrant families live in poverty, and experience a lack of access to quality health care (Elmelech et al., 2002, 2). The low socioeconomic status of many immigrant families places them at increased risk for poor health outcomes and inadequate health care. Additionally, barriers such as limited English proficiency and lack of familiarity with the US healthcare system add to this risk (Derose et al., 2009, 356). The present study examines the physical characteristics of the neighborhood and the relationship of these characteristics to the health insurance status of Black immigrant children. The study is consistent with the national health initiative of Healthy People 2020, which calls attention to children between the ages of 0-17 living in poverty, increased proportion of insured persons, and the furthering of the concepts of social support and community contexts (United States Department of Health and Human Services, 2013).BLACK IMMIGRANTSBlack immigrants are a severely understudied population even though this group has been growing at a remarkable rate over the past 35 years (Kent, 2007; Anderson, 2015). As of 2013, there are 1.8 million African immigrants living in the United States as compared to 881,000 in 2000 (Anderson, 2015). In the United States, there are 1.3 million children in Black immigrant families and they account for 11 percent of Black children in America (Migration Policy Institute, 2016). According to the 2008-2010 American Community Survey, non-Hispanic Black immigrants represented 7.6 percent of the US population and experienced higher child poverty rates, at 36 percent, versus the national average of 19.9 percent (Grieco et al., 2012, 14). Between 2000 and 2013, Africans had the fastest immigrant growth rate compare to other major immigrant groups, increasing by 41 percent during this time period. Statistics from 2014 reveal that 9 percent of immigrant children were Black/African American and that 11 percent of second-generation children were Black/African American (Child Trends, 2014).NEIGHBORHOOD CHARACTERISTICSThe neighborhood socioeconomic environment plays an important role in the health of immigrant families (Winkleby and Cubbin, 2003, 444) yet there has been limited research examining the effect of neighborhood status on immigrants and their families. Researchers have suggested that racial differences in socioeconomic status (SES) contribute to racial health disparities (Williams et al., 2010, 69). According to Winkleby and Cubbin, Black families live in neighborhoods with lower SES status compared to White families (2003, 446). Due to an increase in immigration in the Black community, Black immigrants are even more likely to live in low SES neighborhoods as a result of the migration influx (Kent, 2007, 11; Winkleby and Cubbin, 2003, 451; Martin and Midgley, 2010).Neighborhood and community characteristics have been documented in the literature as influencing health, even after controlling for individual-level factors and individual socioeconomic status. The physical environment of a neighborhood can encourage or discourage community members from participating in outdoor physical activity or social integration. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,073
Score d'incertitude au seuil0,145

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,049
Tête enseignante GPT0,308
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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