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
Bibliographic record
Abstract
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. …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".