Bidirectional Relationship Between Nutrition and Mental Health and Its Impact on the Health of Canadian Immigrants: An Integrative Review
Bibliographic record
Abstract
Background: Immigration is a key factor contributing to population growth in Canada, a trend that is expected to continue. Immigrants generally arrive with better health than the Canadian-born population, but this advantage often diminishes over time, partially due to dietary acculturation. Emerging evidence points to a bidirectional link between nutrition and mental health. Objective: To explore the bidirectional relationship between nutrition and mental health and its impact on the health of Canadian immigrants, with a specific focus on immigrants’ mental health and the healthy immigrant effect. Methods: For this integrative review, two comprehensive literature searches were conducted in the databases MEDLINE, CINAHL, Embase, PsycINFO, Scopus, and Web of Science from inception to July 2024. The review adhered to Whittemore and Knafl’s integrative methodology, with the Mixed Methods Assessment Tool used to assess the quality of the studies. Results: A total of 42 and 34 scientific articles were included from the first and second literature searches, respectively. Four main themes emerged from the literature: (1) food insecurity and mental health, (2) obesity and mental health, (3) diet quality and mental health, and (4) the gut microbiome and mental health. These themes were explored in the context of Canadian immigrants’ health. Conclusions: The health of immigrants to Canada is likely shaped by complex, bidirectional interactions among various determinants of health, influencing both physical and mental well-being. As newcomers are expected to form an increasing proportion of the Canadian population, further research is needed to understand how the interaction between nutrition and mental health can help promote and safeguard the health of Canadian immigrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".