Food environment interactions after migration: a scoping review on low- and middle-income country immigrants in high-income countries
Bibliographic record
Abstract
OBJECTIVE: To map and characterise the interactions between the food environment and immigrant populations from low- and middle-income countries living in high-income countries. DESIGN: A scoping review was carried out following the framework outlined by Arksey and O’Malley, as well as Levac et al. Peer-reviewed studies in English published between 2007 and 2021 were included. Two reviewers screened and selected the papers according to predefined inclusion criteria and reporting of results follows the PRISMA-ScR guidelines. A ‘Best fit’ framework synthesis was carried out using the Analysis Grid for Environments Linked to Obesity (ANGELO) framework. SETTING: High-income countries. PARTICIPANTS: Immigrants from low- and middle-income countries. RESULTS: A total of sixty-eight articles were included, primarily based in the USA, as well as Canada, Australia and Europe, with immigrants originating from five regions of the globe. The analysis identified three overarching themes that interconnected different aspects of the food environment in addition to the four themes of the ANGELO framework. They demonstrate that in valuing fresh, healthy and traditional foods, immigrants were compelled to surpass barriers in order to acquire these, though children’s demands, low incomes, time scarcity and mobility influenced the healthiness of the foods acquired. CONCLUSION: This study brought together evidence on interactions between immigrant populations and the food environment. Immigrants attempted to access fresh, traditional, healthier food, though they faced structural and family-level barriers that impacted the healthiness of the food they acquired. Understanding the food environment and interactions therein is key to proposing interventions and policies that can potentially impact the most vulnerable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".