The food (in)security and mental health nexus in high-risk immigrant populations in middle- and high-income countries: A scoping review
Bibliographic record
Abstract
Pre- and post-migration experiences predispose high-risk immigrants and refugees to elevated risk of food insecurity and negative mental health status. With a significant increase in the occurrence of these challenges in high-risk immigrant populations and the evidence for a syndemic interaction, the need for reasonable efforts and evidence-based interventions to effectively address food insecurity and mental health issues. Such studies could focus on exploring the reinforcing connections between food insecurity and mental health trends. As an initial step to filling in this knowledge gap, we conducted a review of existing literature to explore the commonalities in food security and mental health trends in studies of high-risk immigrants. A systematic search in four major databases (MEDLINE-OVID, CINAHL, Academic Plus, and PubMed) identified 22 relevant articles. A synthesis of these articles identified different subgroups of high-risk immigrants including pregnant women, refugee parents, women immigrants and newcomer youth that experience increased risk of mental health and food insecurity challenges, which are associated with the pre- and post-migration contexts. The experiences of political, social and other environmental violence in countries of origin are traumatic experiences embodied by most high-risk immigrants. On arrival in destination countries, immigrants experience systemic and social barriers that also influence their mental health and food security status. However, access to culturally familiar foods is associated with improved health and wellbeing. Among high-risk immigrants, food, particularly culturally familiar foods are viewed as a symbolic vehicle and a representation of home. When culturally familiar foods are accessible, there is improved mental health status reflected in the ability of immigrants to share their cultures and identity while rebuilding their social networks and enhancing a sense of belonging. Efforts to effectively address food insecurity and mental health challenge in high-risk immigrants require a deep appreciation and understanding of the place and community-based factors and the available resources relevant to the unique needs and experiences of the high-risk immigrant populations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".