Immigration Status and Household Income as Predictors of Childhood Obesity
Bibliographic record
Abstract
Childhood obesity rates have been on a remarkably steep rise in recent years. According to recent literature, the rate in the United States alone has more than doubled compared to other regions in the world such as Australia, Canada and Europe1,2 . Especially in the African immigrant community, there has been speculation of a higher prevalence of childhood obesity in the United States3 . This warranted further investigation into demographic factors impacting childhood obesity rates and their subsequent consequences with other clinical diseases such as diabetes and heart disease. The CDC reports that in 2011-2014, among individuals ages 2 to 19, the prevalence of obesity decreased as the head of household’s level of education increased 3 . However, looking at the country of origin for the head of the household mixed with an objective approach to evaluate glycemic control is a novel approach. The focus of the project was to investigate correlation between factors such as immigration status and its relationship to metabolic risk factors predisposing them to other comorbidities in the pediatric population. The datasets used in the study were derived from the Centers for Disease Control’s (CDC) National Health and Nutrition Examination Survey (NHANES) database. For the 2011-2012 dataset, when comparing the mean HbA1c score of obese children, those whose Household reference person was born outside the USA and had some college education or above showed a significantly higher mean HbA1c score compared to the obese children whose Household reference person was born in the USA with the same college education level. There is statistical significance in the means between country of birth and education level (p=0.04).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".