Influence of Food Environments on the Dietary Patterns of Ghanaian Immigrants in the United States: A Review
Bibliographic record
Abstract
Ghanaian immigrants represent a rapidly growing share of Sub-Saharan African immigrants in the U.S., yet little is known about their dietary patterns. This literature review examined how migration and the U.S. food environment influence Ghanaian immigrants’ diets, with comparisons to peers in the U.K., Canada, and Australia. A comprehensive literature search was conducted on Ghanaian immigrants' dietary patterns and food environment in the U.S., U.K., Canada, and Australia. Findings show that dietary acculturation, U.S. food environment impact, and barriers such as access and cost influenced the dietary practices of Ghanaian immigrants and their children. The limited research highlights a critical gap in understanding the nutrition and health needs of Ghanaian immigrants in the U.S. Additional research studies focused solely on the dietary patterns of Ghanaian immigrants need to be conducted in the U.S. to gather more data on the unique food habits of Ghanaian immigrants. The additional data will help nutrition and dietary policymakers to better understand the nutritional needs and implications of Ghanaian immigrants in the U.S.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".