Food Insecurity and Mental Well-Being in Immigrants: A Global Analysis
Bibliographic record
Abstract
INTRODUCTION: This study estimates the prevalence of food insecurity, mental well-being, and their associations among immigrants and compares the food insecurity-mental well-being associations with nonimmigrants globally and by region. METHODS: The Gallup World Poll data from 2014 to 2019 were analyzed in 2021. A total of 36,313 immigrants and 705,913 nonimmigrants were included. Food insecurity was measured by the Food Insecurity Experience Scale. Mental well-being was assessed using the Negative Experience Index and Positive Experience Index. A community attachment index was used to measure the living environment. Multilevel mixed-effect linear models were used to examine how the Negative Experience Index/Positive Experience Index was associated with food insecurity and the community attachment index in immigrants and nonimmigrants, adjusting for sociodemographic characteristics, survey years, and country fixed effects. The modifying effects of immigration status on food insecurity-mental well-being associations were tested. RESULTS: The weighted proportion of food insecurity among global immigrants was 38.6% during 2014-2019. In the pooled adjusted model, food insecurity was dose-responsively associated with greater Negative Experience Index and lower Positive Experience Index than the food-secure ref (p<0.001 for trend). Similar dose-response associations were observed in nonimmigrants and in region-specific analyses. Community attachment marginally affected the food insecurity-mental well-being associations (all p≤0.001 for interaction). Immigration status significantly modified the food insecurity-mental well-being associations in all analyses (all p=0.01 for interaction), and immigrants experienced poorer mental well-being than nonimmigrants at the same level of community attachment and food insecurity. CONCLUSIONS: Food insecurity is prevalent and is associated with poor mental well-being in immigrants worldwide. Future interventions are needed to alleviate food insecurity and promote community attachment to improve mental health among immigrants, especially in Asian and Pacific countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".