Cultural relevance of food security initiatives and the associated impacts on the cultural identity of immigrants in Canada: A scoping review of food insecurity literature
Bibliographic record
Abstract
• Black-identifying immigrants experience elevated risk of cultural food insecurity. • Charity models for addressing food insecurity are incapable of addressing food needs. • Community food programs embody innate strength in meeting the cultural food needs. • Black-led initiatives and cultural community programs provide ‘more than food’. Evidence shows that Black identifying immigrants in Canada are highly vulnerable to food insecurity and that current interventions do not adequately meet their food needs. However, limited scientific studies exist that have explored the cultural relevance and the service gaps in the food security intervention space for addressing the food needs of Black identifying immigrants in Canada. This research involved a review of literature from relevant databases: CINAHL, OVID-MEDLINE, Academic Plus, and SocINDEX and a review of the gray literature. Qualitative, quantitative, and mixed methods peer-reviewed papers were included if they focused on community food-based programs, services, and initiatives, focused on food-insecure Black-identifying populations in Canada, were written in English, and published between 2000 and 2023. The synthesis reveals several barriers including the lack of familiar food options, transportation, the geographical location of the ethnic food stores, and financial challenges were identified in the existing studies as factors that limit access to the existing interventions by the newcomers. Additionally, sociocultural factors related to perceptions of stigma in the use of charitable support and discrimination in employment and migration that compound the pre-existing vulnerabilities of food-insecure Black identifying immigrants. Black-identifying immigrants are at risk of food and cultural food insecurity due to the limited cultural relevance in the existing interventions. To systematically address escalating food insecurity among Black-identifying individuals in Canada, attention must be directed to the importance of addressing an individual's socio-cultural food needs and acknowledging food as a means to cultural identity that holistically supports overall health.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.036 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".