Food Insecurity and Nutritional Risk among Canadian Newcomer Children in Saskatchewan
Bibliographic record
Abstract
Food insecurity continues to persist among vulnerable groups in Canada, including newcomer families. This mixed-methods study uses an exploratory sequential design to characterize the food security status of newcomer families with children aged 3–13 years. Parents completed food security and 24-hour dietary recall questionnaires, and parents and service providers were interviewed to explore their food insecurity experiences. Fifty percent of participant households experienced food insecurity, while 41% of children were food insecure. More recent newcomer families, and families with parents that had completed high school or some years of postsecondary training, more commonly experienced household food insecurity, compared to families with parents without high school diplomas or those with university degrees. Food-insecure children aged 4–8 years were at higher risk of consuming a lower proportion of energy from protein, lower servings of milk products, and inadequate intakes of vitamin B12 and calcium. Participants identified changes in food buying habits due to low income, using food budgets to purchase prescription drugs and to repay transportation loans, while the school food environment impacted children’s food security. Food security initiatives targeting newcomers may benefit from building on the strengths of newcomers, including traditional dietary practices and willingness to engage in capacity-building programming.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".