Ultra-processed food consumption: an exploration of social determinants among Black children of African and Caribbean descent in Ottawa
Bibliographic record
Abstract
Consumption of ultra-processed foods (UPF) is a growing concern due to its negative impact on diet quality and health outcomes. To our knowledge, no data on UPF consumption are available for specific racial/ethnic children, including Black children, in Canada. This cross-sectional study aimed to explore the social determinants of UPF consumption among Black children of African and Caribbean descent in Ottawa. It included 174 mothers born in the Caribbean or Sub-Saharan Africa and their children aged 6 to 12. A survey was administered to assess demographic and socio-economic characteristics of mothers, children, and households. Children’s dietary intakes were evaluated with a 24 h dietary recall to calculate the proportion of energy from UPF according to the NOVA food classification system. ANOVA and two-step cluster analysis were performed. Identified clusters were compared using chi-square and Student’s t tests. Findings indicate that children whose mothers had been living in Canada longer ( p < 0.001), whose mothers were family-class immigrants ( p = 0.005), and whose households were food secure ( p = 0.049), consumed more UPF than their respective counterparts. Cluster analysis revealed two profiles, named settling and established, reinforcing previous associations. Children in the established profile had a mean energy intake from UPF 9% higher than those in the settling profile ( p = 0.006). This study provided initial findings on the social determinants of UPF consumption among Black children of African and Caribbean descent. It suggests that immigration-related factors and household food security status shaped UPF consumption of these children.
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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 | 0.002 |
| 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".