Exploring Barriers to Food Security Among Immigrants: A Critical Role for Public Health Nutrition
Bibliographic record
Abstract
Upon moving to a new country and new food environment, 2 important public health issues may be experienced by immigrants as they adapt to their new country of residence, namely a higher prevalence of food insecurity and/or a decline in overall health over time postimmigration. Therefore, improving the food environment experienced by new migrants may be an effective strategy to reduce long-term health complications and improve well-being postimmigration. The aim of this paper is to discuss the potential barriers experienced by new immigrants in the access, availability, and utilization of familiar culturally appropriate foods and the subsequent impact on their food security status. Culturally appropriate foods are foods commonly consumed as part of cultural food traditions and are often staples within the diet; however, limited availability of and/or access to these foods can reduce food security. By understanding the barriers to food security and challenges that may be faced by immigrants and refugees, dietitians will be better equipped to assist these individuals in accessing culturally familiar foods and improve quality of life. In this capacity, dietitians can play a critical public health nutrition role by serving as a conduit for new immigrants to access community resources and navigate a new food environment.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".