Prevalence and determinants of food insecurity in migrant Sub-Saharan African and Caribbean households in Ottawa, Canada
Bibliographic record
Abstract
Purpose Food insecurity (FI) is an important social determinant of health and is linked with higher health care costs. There is a high prevalence of FI among recent migrant households in Canada. The purpose of this paper is to evaluate the prevalence of FI in Sub-Saharan African and Caribbean migrants in Ottawa, and to explore determinants of FI in that population. Design/methodology/approach A cross-sectional study was conducted among 190 mothers born in Sub-Saharan Africa or the Caribbean living in Ottawa and having a child between 6 and 12 years old. Health Canada’s Household Food Security Survey Module was used to evaluate participants’ food security in the past 12 months. χ2 tests and multivariate logistic regression analyses were used to measure determinants of FI (n=182). Findings A very high rate of FI (45.1 percent) was found among participants. When numerous determinants of FI were included in a multivariate model, household FI was associated with Caribbean origin, low education attainment, lone motherhood, living in Canada for five years or less and reliance on social assistance. Originality/value These findings highlight the need for FI to be explicitly addressed in migrant integration strategies in order to improve their financial power to purchase sufficient, nutritious and culturally acceptable foods. Enhancing migrants’ access to affordable child care and well-paid jobs, improving social assistance programs and providing more affordable subsidized housing programs could be beneficial.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".