Diet and health of immigrant women of African descent in New York City
Bibliographic record
Abstract
Studies show that Asians and Hispanics change their dietary patterns by consuming foods high in fat, cholesterol, and sugar, and low in dietary fiber after migrating to the US. Few studies describe the dietary patterns and health status of African immigrants in the US. In this study, two 24‐hour dietary recalls were collected to assess the dietary intakes of immigrant women of African descent living or working in New York City. Self‐reported data about acculturation, socioeconomic status, body weight, and family history of chronic diseases were also collected. Consumption of carbohydrate, protein, vitamins A, B6, and B12, thiamin, riboflavin, niacin, and sodium exceeded 100% of the Dietary Reference Intake (DRI); consumption of dietary fiber, folate, vitamins C, D, and E, calcium, iron, magnesium, potassium and zinc were below 100% of the DRIs. Energy intake from protein and carbohydrate were within the acceptable ranges while that from fat was higher than recommended. Approximately 67% of the women were born in Africa, and 44% had completed at least a college degree. Seventeen percent of the women were overweight and obese, respectively. Most women did not have family history of diabetes, hypertension, congestive heart failure, and stroke. Although many of the women appeared healthy, these data suggest that women of African descent may not be meeting all of their nutritional needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".